CINXE.COM
GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with implementations.
<!DOCTYPE html> <html lang="en" data-color-mode="auto" data-light-theme="light" data-dark-theme="dark" data-a11y-animated-images="system" data-a11y-link-underlines="true" > <head> <meta charset="utf-8"> <link rel="dns-prefetch" href="https://github.githubassets.com"> <link rel="dns-prefetch" href="https://avatars.githubusercontent.com"> <link rel="dns-prefetch" href="https://github-cloud.s3.amazonaws.com"> <link rel="dns-prefetch" href="https://user-images.githubusercontent.com/"> <link rel="preconnect" href="https://github.githubassets.com" crossorigin> <link rel="preconnect" href="https://avatars.githubusercontent.com"> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/light-74231a1f3bbb.css" /><link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/dark-8a995f0bacd4.css" /><link data-color-theme="dark_dimmed" crossorigin="anonymous" media="all" rel="stylesheet" data-href="https://github.githubassets.com/assets/dark_dimmed-f37fb7684b1f.css" /><link data-color-theme="dark_high_contrast" crossorigin="anonymous" media="all" rel="stylesheet" data-href="https://github.githubassets.com/assets/dark_high_contrast-9ac301c3ebe5.css" /><link data-color-theme="dark_colorblind" crossorigin="anonymous" media="all" rel="stylesheet" data-href="https://github.githubassets.com/assets/dark_colorblind-cd826e8636dc.css" /><link data-color-theme="light_colorblind" crossorigin="anonymous" media="all" rel="stylesheet" data-href="https://github.githubassets.com/assets/light_colorblind-f91b0f603451.css" /><link data-color-theme="light_high_contrast" crossorigin="anonymous" media="all" rel="stylesheet" data-href="https://github.githubassets.com/assets/light_high_contrast-83beb16e0ecf.css" /><link data-color-theme="light_tritanopia" crossorigin="anonymous" media="all" rel="stylesheet" data-href="https://github.githubassets.com/assets/light_tritanopia-6e122dab64fc.css" /><link data-color-theme="dark_tritanopia" crossorigin="anonymous" media="all" rel="stylesheet" data-href="https://github.githubassets.com/assets/dark_tritanopia-18119e682df0.css" /> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/primer-primitives-225433424a87.css" /> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/primer-aaa714e5674d.css" /> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/global-0a3c53b9d1c2.css" /> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/github-ea73c9cb5377.css" /> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/repository-4fce88777fa8.css" /> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/code-0210be90f4d3.css" /> <script type="application/json" id="client-env">{"locale":"en","featureFlags":["a11y_quote_reply_fix","copilot_immersive_issue_preview","copilot_new_references_ui","copilot_chat_repo_custom_instructions_preview","copilot_no_floating_button","copilot_topics_as_references","copilot_read_shared_conversation","copilot_duplicate_thread","copilot_buffered_streaming","dotcom_chat_client_side_skills","experimentation_azure_variant_endpoint","failbot_handle_non_errors","geojson_azure_maps","ghost_pilot_confidence_truncation_25","ghost_pilot_confidence_truncation_40","github_models_gateway_parse_params","github_models_o3_mini_streaming","insert_before_patch","issues_react_remove_placeholders","issues_react_blur_item_picker_on_close","marketing_pages_search_explore_provider","primer_react_css_modules_ga","react_data_router_pull_requests","remove_child_patch","sample_network_conn_type","swp_enterprise_contact_form","site_proxima_australia_update","viewscreen_sandbox","issues_react_create_milestone","issues_react_cache_fix_workaround","lifecycle_label_name_updates","copilot_task_oriented_assistive_prompts","issue_types_prevent_private_type_creation","refresh_image_video_src","react_router_dispose_on_disconnect","codespaces_prebuild_region_target_update","turbo_app_id_restore","copilot_code_review_sign_up_closed"]}</script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/wp-runtime-8835fef01a5a.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_oddbird_popover-polyfill_dist_popover_js-9da652f58479.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_arianotify-polyfill_ariaNotify-polyfill_js-node_modules_github_mi-3abb8f-46b9f4874d95.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/ui_packages_failbot_failbot_ts-75968cfb5298.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/environment-f04cb2a9fc8c.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_primer_behaviors_dist_esm_index_mjs-0dbb79f97f8f.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_selector-observer_dist_index_esm_js-f690fd9ae3d5.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_relative-time-element_dist_index_js-62d275b7ddd9.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_text-expander-element_dist_index_js-78748950cb0c.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_auto-complete-element_dist_index_js-node_modules_github_catalyst_-8e9f78-a90ac05d2469.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_filter-input-element_dist_index_js-node_modules_github_remote-inp-b5f1d7-a1760ffda83d.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_markdown-toolbar-element_dist_index_js-ceef33f593fa.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_file-attachment-element_dist_index_js-node_modules_primer_view-co-c44a69-efa32db3a345.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/github-elements-394f8eb34f19.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/element-registry-25113a65b77f.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_braintree_browser-detection_dist_browser-detection_js-node_modules_githu-2906d7-2a07a295af40.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_lit-html_lit-html_js-be8cb88f481b.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_mini-throttle_dist_index_js-node_modules_morphdom_dist_morphdom-e-7c534c-a4a1922eb55f.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_turbo_dist_turbo_es2017-esm_js-a03ee12d659a.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_remote-form_dist_index_js-node_modules_delegated-events_dist_inde-893f9f-b6294cf703b7.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_color-convert_index_js-e3180fe3bcb3.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_quote-selection_dist_index_js-node_modules_github_session-resume_-947061-e7a6c4a19f98.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/ui_packages_updatable-content_updatable-content_ts-2a55124d5c52.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/app_assets_modules_github_behaviors_task-list_ts-app_assets_modules_github_sso_ts-ui_packages-900dde-768abe60b1f8.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/app_assets_modules_github_sticky-scroll-into-view_ts-3e000c5d31a9.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/app_assets_modules_github_behaviors_ajax-error_ts-app_assets_modules_github_behaviors_include-87a4ae-4c160a67a3f8.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/app_assets_modules_github_behaviors_commenting_edit_ts-app_assets_modules_github_behaviors_ht-83c235-e429cff6ceb1.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/behaviors-f41565a56b0d.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_delegated-events_dist_index_js-node_modules_github_catalyst_lib_index_js-f6223d90c7ba.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/notifications-global-01e85cd1be94.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_virtualized-list_es_index_js-node_modules_github_template-parts_lib_index_js-94dc7a2157c1.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_remote-form_dist_index_js-node_modules_delegated-events_dist_inde-70450e-4b93df70b903.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/app_assets_modules_github_ref-selector_ts-3e9d848bab5f.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/codespaces-c3bcacfe317c.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_filter-input-element_dist_index_js-node_modules_github_remote-inp-3eebbd-0763620ad7bf.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_mini-throttle_dist_decorators_js-node_modules_delegated-events_di-e161aa-9d41fb1b6c9e.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_file-attachment-element_dist_index_js-node_modules_github_remote--3c9c82-b71ef90fbdc7.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/repositories-fc1c2cf0d1c0.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_mini-throttle_dist_index_js-node_modules_github_catalyst_lib_inde-dbbea9-26cce2010167.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/code-menu-1c0aedc134b1.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/primer-react-602097a4b0db.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/react-core-a18127980111.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/react-lib-f1bca44e0926.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/octicons-react-cf2f2ab8dab4.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_emotion_is-prop-valid_dist_emotion-is-prop-valid_esm_js-node_modules_emo-62da9f-2df2f32ec596.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_mini-throttle_dist_index_js-node_modules_stacktrace-parser_dist_s-e7dcdd-9a233856b02c.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_oddbird_popover-polyfill_dist_popover-fn_js-55fea94174bf.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/notifications-subscriptions-menu-57956eade845.js"></script> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/primer-react.248e2356ac373ce2e5c1.module.css" /> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/notifications-subscriptions-menu.1bcff9205c241e99cff2.module.css" /> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/primer-react.248e2356ac373ce2e5c1.module.css" /> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/notifications-subscriptions-menu.1bcff9205c241e99cff2.module.css" /> <title>GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with implementations.</title> <meta name="route-pattern" content="/:user_id/:repository" data-turbo-transient> <meta name="route-controller" content="files" data-turbo-transient> <meta name="route-action" content="disambiguate" data-turbo-transient> <meta name="current-catalog-service-hash" content="f3abb0cc802f3d7b95fc8762b94bdcb13bf39634c40c357301c4aa1d67a256fb"> <meta name="request-id" content="A43C:CDB2B:1B51B5E:20A1860:67E6F233" data-pjax-transient="true"/><meta name="html-safe-nonce" content="a7f410e3380ea743f5adb14689f42849a1f48d1a1fdfcf204d8ee6c4e53c5f17" data-pjax-transient="true"/><meta name="visitor-payload" content="eyJyZWZlcnJlciI6IiIsInJlcXVlc3RfaWQiOiJBNDNDOkNEQjJCOjFCNTFCNUU6MjBBMTg2MDo2N0U2RjIzMyIsInZpc2l0b3JfaWQiOiIxMTQxOTc0NjI2ODQ5OTc2ODgzIiwicmVnaW9uX2VkZ2UiOiJzb3V0aGVhc3Rhc2lhIiwicmVnaW9uX3JlbmRlciI6InNvdXRoZWFzdGFzaWEifQ==" data-pjax-transient="true"/><meta name="visitor-hmac" content="81d29ea48ed3793a6132572fbdf1a01821657125e1c283f83fb05a31eaae9d12" data-pjax-transient="true"/> <meta name="hovercard-subject-tag" content="repository:186163475" data-turbo-transient> <meta name="github-keyboard-shortcuts" content="repository,copilot" data-turbo-transient="true" /> <meta name="selected-link" value="repo_source" data-turbo-transient> <link rel="assets" href="https://github.githubassets.com/"> <meta name="google-site-verification" content="Apib7-x98H0j5cPqHWwSMm6dNU4GmODRoqxLiDzdx9I"> <meta name="octolytics-url" content="https://collector.github.com/github/collect" /> <meta name="analytics-location" content="/<user-name>/<repo-name>" data-turbo-transient="true" /> <meta name="user-login" content=""> <meta name="viewport" content="width=device-width"> <meta name="description" content="A curated list of gradient boosting research papers with implementations. - GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with implementations."> <link rel="search" type="application/opensearchdescription+xml" href="/opensearch.xml" title="GitHub"> <link rel="fluid-icon" href="https://github.com/fluidicon.png" title="GitHub"> <meta property="fb:app_id" content="1401488693436528"> <meta name="apple-itunes-app" content="app-id=1477376905, app-argument=https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers" /> <meta name="twitter:image" content="https://repository-images.githubusercontent.com/186163475/acd05400-7d84-11e9-8f05-84fedee77748" /><meta name="twitter:site" content="@github" /><meta name="twitter:card" content="summary_large_image" /><meta name="twitter:title" content="GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with implementations." /><meta name="twitter:description" content="A curated list of gradient boosting research papers with implementations. - GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with ..." /> <meta property="og:image" content="https://repository-images.githubusercontent.com/186163475/acd05400-7d84-11e9-8f05-84fedee77748" /><meta property="og:image:alt" content="A curated list of gradient boosting research papers with implementations. - GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with ..." /><meta property="og:site_name" content="GitHub" /><meta property="og:type" content="object" /><meta property="og:title" content="GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with implementations." /><meta property="og:url" content="https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers" /><meta property="og:description" content="A curated list of gradient boosting research papers with implementations. - GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with ..." /> <meta name="hostname" content="github.com"> <meta name="expected-hostname" content="github.com"> <meta http-equiv="x-pjax-version" content="9c2c29b39626a34dc55824f953fe2c2cd24d35caad0cb2b70eed98933f17a7c8" data-turbo-track="reload"> <meta http-equiv="x-pjax-csp-version" content="77190eb53eb47fc30bd2fcc17a7eefa2dfd8505869fee9299ba911be3a40a9eb" data-turbo-track="reload"> <meta http-equiv="x-pjax-css-version" content="159e03504eed5183f9787c72780a7d8c1460af30746ab09d728b048c41719efa" data-turbo-track="reload"> <meta http-equiv="x-pjax-js-version" content="373db3a6b0664eccf6234c8178e06da8f1708411ca11db871056537ed1a47cc3" data-turbo-track="reload"> <meta name="turbo-cache-control" content="no-preview" data-turbo-transient=""> <meta data-hydrostats="publish"> <meta name="go-import" content="github.com/benedekrozemberczki/awesome-gradient-boosting-papers git https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers.git"> <meta name="octolytics-dimension-user_id" content="17380887" /><meta name="octolytics-dimension-user_login" content="benedekrozemberczki" /><meta name="octolytics-dimension-repository_id" content="186163475" /><meta name="octolytics-dimension-repository_nwo" content="benedekrozemberczki/awesome-gradient-boosting-papers" /><meta name="octolytics-dimension-repository_public" content="true" /><meta name="octolytics-dimension-repository_is_fork" content="false" /><meta name="octolytics-dimension-repository_network_root_id" content="186163475" /><meta name="octolytics-dimension-repository_network_root_nwo" content="benedekrozemberczki/awesome-gradient-boosting-papers" /> <link rel="canonical" href="https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers" data-turbo-transient> <meta name="turbo-body-classes" content="logged-out env-production page-responsive"> <meta name="browser-stats-url" content="https://api.github.com/_private/browser/stats"> <meta name="browser-errors-url" content="https://api.github.com/_private/browser/errors"> <meta name="release" content="356c11eac0ad211f55ef49f5ee50b47c1f7a2912"> <link rel="mask-icon" href="https://github.githubassets.com/assets/pinned-octocat-093da3e6fa40.svg" color="#000000"> <link rel="alternate icon" class="js-site-favicon" type="image/png" href="https://github.githubassets.com/favicons/favicon.png"> <link rel="icon" class="js-site-favicon" type="image/svg+xml" href="https://github.githubassets.com/favicons/favicon.svg" data-base-href="https://github.githubassets.com/favicons/favicon"> <meta name="theme-color" content="#1e2327"> <meta name="color-scheme" content="light dark" /> <link rel="manifest" href="/manifest.json" crossOrigin="use-credentials"> </head> <body class="logged-out env-production page-responsive" style="word-wrap: break-word;"> <div data-turbo-body class="logged-out env-production page-responsive" style="word-wrap: break-word;"> <div class="position-relative header-wrapper js-header-wrapper "> <a href="#start-of-content" data-skip-target-assigned="false" class="px-2 py-4 color-bg-accent-emphasis color-fg-on-emphasis show-on-focus js-skip-to-content">Skip to content</a> <span data-view-component="true" class="progress-pjax-loader Progress position-fixed width-full"> <span style="width: 0%;" data-view-component="true" class="Progress-item progress-pjax-loader-bar left-0 top-0 color-bg-accent-emphasis"></span> </span> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/ui_packages_ui-commands_ui-commands_ts-2ea4e93613c0.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/keyboard-shortcuts-dialog-79d6a754ebf9.js"></script> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/primer-react.248e2356ac373ce2e5c1.module.css" /> <react-partial partial-name="keyboard-shortcuts-dialog" data-ssr="false" data-attempted-ssr="false" > <script type="application/json" data-target="react-partial.embeddedData">{"props":{"docsUrl":"https://docs.github.com/get-started/accessibility/keyboard-shortcuts"}}</script> <div data-target="react-partial.reactRoot"></div> </react-partial> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_github_remote-form_dist_index_js-node_modules_delegated-events_dist_inde-94fd67-4898d1bf4b51.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/sessions-730dca81d0a2.js"></script> <header class="HeaderMktg header-logged-out js-details-container js-header Details f4 py-3" role="banner" data-is-top="true" data-color-mode=light data-light-theme=light data-dark-theme=dark> <h2 class="sr-only">Navigation Menu</h2> <button type="button" class="HeaderMktg-backdrop d-lg-none border-0 position-fixed top-0 left-0 width-full height-full js-details-target" aria-label="Toggle navigation"> <span class="d-none">Toggle navigation</span> </button> <div class="d-flex flex-column flex-lg-row flex-items-center px-3 px-md-4 px-lg-5 height-full position-relative z-1"> <div class="d-flex flex-justify-between flex-items-center width-full width-lg-auto"> <div class="flex-1"> <button aria-label="Toggle navigation" aria-expanded="false" type="button" data-view-component="true" class="js-details-target js-nav-padding-recalculate js-header-menu-toggle Button--link Button--medium Button d-lg-none color-fg-inherit p-1"> <span class="Button-content"> <span class="Button-label"><div class="HeaderMenu-toggle-bar rounded my-1"></div> <div class="HeaderMenu-toggle-bar rounded my-1"></div> <div class="HeaderMenu-toggle-bar rounded my-1"></div></span> </span> </button> </div> <a class="mr-lg-3 color-fg-inherit flex-order-2 js-prevent-focus-on-mobile-nav" href="/" aria-label="Homepage" data-analytics-event="{"category":"Marketing nav","action":"click to go to homepage","label":"ref_page:Marketing;ref_cta:Logomark;ref_loc:Header"}"> <svg height="32" aria-hidden="true" viewBox="0 0 24 24" version="1.1" width="32" data-view-component="true" class="octicon octicon-mark-github"> <path d="M12 1C5.9225 1 1 5.9225 1 12C1 16.8675 4.14875 20.9787 8.52125 22.4362C9.07125 22.5325 9.2775 22.2025 9.2775 21.9137C9.2775 21.6525 9.26375 20.7862 9.26375 19.865C6.5 20.3737 5.785 19.1912 5.565 18.5725C5.44125 18.2562 4.905 17.28 4.4375 17.0187C4.0525 16.8125 3.5025 16.3037 4.42375 16.29C5.29 16.2762 5.90875 17.0875 6.115 17.4175C7.105 19.0812 8.68625 18.6137 9.31875 18.325C9.415 17.61 9.70375 17.1287 10.02 16.8537C7.5725 16.5787 5.015 15.63 5.015 11.4225C5.015 10.2262 5.44125 9.23625 6.1425 8.46625C6.0325 8.19125 5.6475 7.06375 6.2525 5.55125C6.2525 5.55125 7.17375 5.2625 9.2775 6.67875C10.1575 6.43125 11.0925 6.3075 12.0275 6.3075C12.9625 6.3075 13.8975 6.43125 14.7775 6.67875C16.8813 5.24875 17.8025 5.55125 17.8025 5.55125C18.4075 7.06375 18.0225 8.19125 17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 15.5063 22.4362C19.8513 20.9787 23 16.8537 23 12C23 5.9225 18.0775 1 12 1Z"></path> </svg> </a> <div class="flex-1 flex-order-2 text-right"> <a href="/login?return_to=https%3A%2F%2Fgithub.com%2Fbenedekrozemberczki%2Fawesome-gradient-boosting-papers" class="HeaderMenu-link HeaderMenu-button d-inline-flex d-lg-none flex-order-1 f5 no-underline border color-border-default rounded-2 px-2 py-1 color-fg-inherit js-prevent-focus-on-mobile-nav" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"site header menu","repository_id":null,"auth_type":"SIGN_UP","originating_url":"https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers","user_id":null}}" data-hydro-click-hmac="89ebe38c4ba8405160fc724ae57008a8c3ac3c9cbb25eb46ad707403f3ae6a7c" data-analytics-event="{"category":"Marketing nav","action":"click to Sign in","label":"ref_page:Marketing;ref_cta:Sign in;ref_loc:Header"}" > Sign in </a> </div> </div> <div class="HeaderMenu js-header-menu height-fit position-lg-relative d-lg-flex flex-column flex-auto top-0"> <div class="HeaderMenu-wrapper d-flex flex-column flex-self-start flex-lg-row flex-auto rounded rounded-lg-0"> <nav class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Product <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">Security</div> Find and fix vulnerabilities </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"actions","context":"product","tag":"link","label":"actions_link_product_navbar"}" href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-issue-opened color-fg-subtle mr-3"> <path d="M12 1c6.075 0 11 4.925 11 11s-4.925 11-11 11S1 18.075 1 12 5.925 1 12 1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Zm9.5 2a2 2 0 1 1-.001-3.999A2 2 0 0 1 12 14Z"></path> </svg> <div> <div class="color-fg-default h4">Issues</div> Plan and track work </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"code_review","context":"product","tag":"link","label":"code_review_link_product_navbar"}" href="https://github.com/features/code-review"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-code-review color-fg-subtle mr-3"> <path d="M10.3 6.74a.75.75 0 0 1-.04 1.06l-2.908 2.7 2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M1.5 4.25c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v12.5a1.75 1.75 0 0 1-1.75 1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"discussions","context":"product","tag":"link","label":"discussions_link_product_navbar"}" href="https://github.com/features/discussions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 15.543V14H1.75A1.75 1.75 0 0 1 0 12.25v-9.5C0 1.784.784 1 1.75 1ZM1.5 2.75v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25Z"></path><path d="M22.5 8.75a.25.25 0 0 0-.25-.25h-3.5a.75.75 0 0 1 0-1.5h3.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 22.25 20H21v1.543a1.457 1.457 0 0 1-2.487 1.03L15.939 20H10.75A1.75 1.75 0 0 1 9 18.25v-1.465a.75.75 0 0 1 1.5 0v1.465c0 .138.112.25.25.25h5.5a.75.75 0 0 1 .53.22l2.72 2.72v-2.19a.75.75 0 0 1 .75-.75h2a.25.25 0 0 0 .25-.25v-9.5Z"></path> </svg> <div> <div class="color-fg-default h4">Discussions</div> Collaborate outside of code </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"code_search","context":"product","tag":"link","label":"code_search_link_product_navbar"}" href="https://github.com/features/code-search"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-code-square color-fg-subtle mr-3"> <path d="M10.3 8.24a.75.75 0 0 1-.04 1.06L7.352 12l2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M2 3.75C2 2.784 2.784 2 3.75 2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="product-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="product-explore-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"all_features","context":"product","tag":"link","label":"all_features_link_product_navbar"}" href="https://github.com/features"> All features </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"documentation","context":"product","tag":"link","label":"documentation_link_product_navbar"}" href="https://docs.github.com"> Documentation <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"blog","context":"product","tag":"link","label":"blog_link_product_navbar"}" href="https://github.blog"> Blog <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 pb-lg-3 mb-3 mb-lg-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-company-size-heading">By company size</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-company-size-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"enterprises","context":"solutions","tag":"link","label":"enterprises_link_solutions_navbar"}" href="https://github.com/enterprise"> Enterprises </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"small_and_medium_teams","context":"solutions","tag":"link","label":"small_and_medium_teams_link_solutions_navbar"}" href="https://github.com/team"> Small and medium teams </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"startups","context":"solutions","tag":"link","label":"startups_link_solutions_navbar"}" href="https://github.com/enterprise/startups"> Startups </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"nonprofits","context":"solutions","tag":"link","label":"nonprofits_link_solutions_navbar"}" href="/solutions/industry/nonprofits"> Nonprofits </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-use-case-heading">By use case</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-use-case-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"devsecops","context":"solutions","tag":"link","label":"devsecops_link_solutions_navbar"}" href="/solutions/use-case/devsecops"> DevSecOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"devops","context":"solutions","tag":"link","label":"devops_link_solutions_navbar"}" href="/solutions/use-case/devops"> DevOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"ci_cd","context":"solutions","tag":"link","label":"ci_cd_link_solutions_navbar"}" href="/solutions/use-case/ci-cd"> CI/CD </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_use_cases","context":"solutions","tag":"link","label":"view_all_use_cases_link_solutions_navbar"}" href="/solutions/use-case"> View all use cases </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-industry-heading">By industry</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-industry-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"healthcare","context":"solutions","tag":"link","label":"healthcare_link_solutions_navbar"}" href="/solutions/industry/healthcare"> Healthcare </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"financial_services","context":"solutions","tag":"link","label":"financial_services_link_solutions_navbar"}" href="/solutions/industry/financial-services"> Financial services </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"manufacturing","context":"solutions","tag":"link","label":"manufacturing_link_solutions_navbar"}" href="/solutions/industry/manufacturing"> Manufacturing </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"government","context":"solutions","tag":"link","label":"government_link_solutions_navbar"}" href="/solutions/industry/government"> Government </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_industries","context":"solutions","tag":"link","label":"view_all_industries_link_solutions_navbar"}" href="/solutions/industry"> View all industries </a></li> </ul> </div> </div> <div class="HeaderMenu-trailing-link rounded-bottom-2 flex-shrink-0 mt-lg-4 px-lg-4 py-4 py-lg-3 f5 text-semibold"> <a href="/solutions"> View all solutions <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-right HeaderMenu-trailing-link-icon"> <path d="M6.22 3.22a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L9.94 8 6.22 4.28a.75.75 0 0 1 0-1.06Z"></path> </svg> </a> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Resources <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="resources-topics-heading">Topics</span> <ul class="list-style-none f5" aria-labelledby="resources-topics-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"ai","context":"resources","tag":"link","label":"ai_link_resources_navbar"}" href="/resources/articles/ai"> AI </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"devops","context":"resources","tag":"link","label":"devops_link_resources_navbar"}" href="/resources/articles/devops"> DevOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"security","context":"resources","tag":"link","label":"security_link_resources_navbar"}" href="/resources/articles/security"> Security </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"software_development","context":"resources","tag":"link","label":"software_development_link_resources_navbar"}" href="/resources/articles/software-development"> Software Development </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all","context":"resources","tag":"link","label":"view_all_link_resources_navbar"}" href="/resources/articles"> View all </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="resources-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="resources-explore-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"learning_pathways","context":"resources","tag":"link","label":"learning_pathways_link_resources_navbar"}" href="https://resources.github.com/learn/pathways"> Learning Pathways <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"events_amp_webinars","context":"resources","tag":"link","label":"events_amp_webinars_link_resources_navbar"}" href="https://resources.github.com"> Events & Webinars <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"ebooks_amp_whitepapers","context":"resources","tag":"link","label":"ebooks_amp_whitepapers_link_resources_navbar"}" href="https://github.com/resources/whitepapers"> Ebooks & Whitepapers </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"customer_stories","context":"resources","tag":"link","label":"customer_stories_link_resources_navbar"}" href="https://github.com/customer-stories"> Customer Stories </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"partners","context":"resources","tag":"link","label":"partners_link_resources_navbar"}" href="https://partner.github.com"> Partners <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"executive_insights","context":"resources","tag":"link","label":"executive_insights_link_resources_navbar"}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Open Source <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"the_readme_project","context":"open_source","tag":"link","label":"the_readme_project_link_open_source_navbar"}" href="https://github.com/readme"> <div> <div class="color-fg-default h4">The ReadME Project</div> GitHub community articles </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="open-source-repositories-heading">Repositories</span> <ul class="list-style-none f5" aria-labelledby="open-source-repositories-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"topics","context":"open_source","tag":"link","label":"topics_link_open_source_navbar"}" href="https://github.com/topics"> Topics </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"trending","context":"open_source","tag":"link","label":"trending_link_open_source_navbar"}" href="https://github.com/trending"> Trending </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"collections","context":"open_source","tag":"link","label":"collections_link_open_source_navbar"}" href="https://github.com/collections"> Collections </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Enterprise <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"enterprise_platform","context":"enterprise","tag":"link","label":"enterprise_platform_link_enterprise_navbar"}" href="/enterprise"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-stack color-fg-subtle mr-3"> <path d="M11.063 1.456a1.749 1.749 0 0 1 1.874 0l8.383 5.316a1.751 1.751 0 0 1 0 2.956l-8.383 5.316a1.749 1.749 0 0 1-1.874 0L2.68 9.728a1.751 1.751 0 0 1 0-2.956Zm1.071 1.267a.25.25 0 0 0-.268 0L3.483 8.039a.25.25 0 0 0 0 .422l8.383 5.316a.25.25 0 0 0 .268 0l8.383-5.316a.25.25 0 0 0 0-.422Z"></path><path d="M1.867 12.324a.75.75 0 0 1 1.035-.232l8.964 5.685a.25.25 0 0 0 .268 0l8.964-5.685a.75.75 0 0 1 .804 1.267l-8.965 5.685a1.749 1.749 0 0 1-1.874 0l-8.965-5.685a.75.75 0 0 1-.231-1.035Z"></path><path d="M1.867 16.324a.75.75 0 0 1 1.035-.232l8.964 5.685a.25.25 0 0 0 .268 0l8.964-5.685a.75.75 0 0 1 .804 1.267l-8.965 5.685a1.749 1.749 0 0 1-1.874 0l-8.965-5.685a.75.75 0 0 1-.231-1.035Z"></path> </svg> <div> <div class="color-fg-default h4">Enterprise platform</div> AI-powered developer platform </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="enterprise-available-add-ons-heading">Available add-ons</span> <ul class="list-style-none f5" aria-labelledby="enterprise-available-add-ons-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"advanced_security","context":"enterprise","tag":"link","label":"advanced_security_link_enterprise_navbar"}" href="https://github.com/enterprise/advanced-security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">Advanced Security</div> Enterprise-grade security features </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"copilot_for_business","context":"enterprise","tag":"link","label":"copilot_for_business_link_enterprise_navbar"}" href="/features/copilot/copilot-business"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">Copilot for business</div> Enterprise-grade AI features </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"premium_support","context":"enterprise","tag":"link","label":"premium_support_link_enterprise_navbar"}" href="/premium-support"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 15.543V14H1.75A1.75 1.75 0 0 1 0 12.25v-9.5C0 1.784.784 1 1.75 1ZM1.5 2.75v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25Z"></path><path d="M22.5 8.75a.25.25 0 0 0-.25-.25h-3.5a.75.75 0 0 1 0-1.5h3.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 22.25 20H21v1.543a1.457 1.457 0 0 1-2.487 1.03L15.939 20H10.75A1.75 1.75 0 0 1 9 18.25v-1.465a.75.75 0 0 1 1.5 0v1.465c0 .138.112.25.25.25h5.5a.75.75 0 0 1 .53.22l2.72 2.72v-2.19a.75.75 0 0 1 .75-.75h2a.25.25 0 0 0 .25-.25v-9.5Z"></path> </svg> <div> <div class="color-fg-default h4">Premium Support</div> Enterprise-grade 24/7 support </div> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <a class="HeaderMenu-link no-underline px-0 px-lg-2 py-3 py-lg-2 d-block d-lg-inline-block" data-analytics-event="{"location":"navbar","action":"pricing","context":"global","tag":"link","label":"pricing_link_global_navbar"}" href="https://github.com/pricing">Pricing</a> </li> </ul> </nav> <div class="d-flex flex-column flex-lg-row width-full flex-justify-end flex-lg-items-center text-center mt-3 mt-lg-0 text-lg-left ml-lg-3"> <qbsearch-input class="search-input" data-scope="repo:benedekrozemberczki/awesome-gradient-boosting-papers" data-custom-scopes-path="/search/custom_scopes" data-delete-custom-scopes-csrf="eE19stoiXPTOyt5tUwCSnx-hAXyj_4GttdmSHj4PdjfibiKWThj0qEOiuo3rJaFn9Kp-CXY6AmhXl-xM1NsHEw" data-max-custom-scopes="10" data-header-redesign-enabled="false" data-initial-value="" data-blackbird-suggestions-path="/search/suggestions" data-jump-to-suggestions-path="/_graphql/GetSuggestedNavigationDestinations" data-current-repository="benedekrozemberczki/awesome-gradient-boosting-papers" data-current-org="" data-current-owner="benedekrozemberczki" data-logged-in="false" data-copilot-chat-enabled="false" data-nl-search-enabled="false" data-retain-scroll-position="true"> <div class="search-input-container search-with-dialog position-relative d-flex flex-row flex-items-center mr-4 rounded" data-action="click:qbsearch-input#searchInputContainerClicked" > <button type="button" class="header-search-button placeholder input-button form-control d-flex flex-1 flex-self-stretch flex-items-center no-wrap width-full py-0 pl-2 pr-0 text-left border-0 box-shadow-none" data-target="qbsearch-input.inputButton" aria-label="Search or jump to…" aria-haspopup="dialog" placeholder="Search or jump to..." data-hotkey=s,/ autocapitalize="off" data-analytics-event="{"location":"navbar","action":"searchbar","context":"global","tag":"input","label":"searchbar_input_global_navbar"}" data-action="click:qbsearch-input#handleExpand" > <div class="mr-2 color-fg-muted"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-search"> <path d="M10.68 11.74a6 6 0 0 1-7.922-8.982 6 6 0 0 1 8.982 7.922l3.04 3.04a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215ZM11.5 7a4.499 4.499 0 1 0-8.997 0A4.499 4.499 0 0 0 11.5 7Z"></path> </svg> </div> <span class="flex-1" data-target="qbsearch-input.inputButtonText">Search or jump to...</span> <div class="d-flex" data-target="qbsearch-input.hotkeyIndicator"> <svg xmlns="http://www.w3.org/2000/svg" width="22" height="20" aria-hidden="true" class="mr-1"><path fill="none" stroke="#979A9C" opacity=".4" d="M3.5.5h12c1.7 0 3 1.3 3 3v13c0 1.7-1.3 3-3 3h-12c-1.7 0-3-1.3-3-3v-13c0-1.7 1.3-3 3-3z"></path><path fill="#979A9C" d="M11.8 6L8 15.1h-.9L10.8 6h1z"></path></svg> </div> </button> <input type="hidden" name="type" class="js-site-search-type-field"> <div class="Overlay--hidden " data-modal-dialog-overlay> <modal-dialog data-action="close:qbsearch-input#handleClose cancel:qbsearch-input#handleClose" data-target="qbsearch-input.searchSuggestionsDialog" role="dialog" id="search-suggestions-dialog" aria-modal="true" aria-labelledby="search-suggestions-dialog-header" data-view-component="true" class="Overlay Overlay--width-large Overlay--height-auto"> <h1 id="search-suggestions-dialog-header" class="sr-only">Search code, repositories, users, issues, pull requests...</h1> <div class="Overlay-body Overlay-body--paddingNone"> <div data-view-component="true"> <div class="search-suggestions position-fixed width-full color-shadow-large border color-fg-default color-bg-default overflow-hidden d-flex flex-column query-builder-container" style="border-radius: 12px;" data-target="qbsearch-input.queryBuilderContainer" hidden > <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="query-builder-test-form" action="" accept-charset="UTF-8" method="get"> <query-builder data-target="qbsearch-input.queryBuilder" id="query-builder-query-builder-test" data-filter-key=":" data-view-component="true" class="QueryBuilder search-query-builder"> <div class="FormControl FormControl--fullWidth"> <label id="query-builder-test-label" for="query-builder-test" class="FormControl-label sr-only"> Search </label> <div class="QueryBuilder-StyledInput width-fit " data-target="query-builder.styledInput" > <span id="query-builder-test-leadingvisual-wrap" class="FormControl-input-leadingVisualWrap QueryBuilder-leadingVisualWrap"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-search FormControl-input-leadingVisual"> <path d="M10.68 11.74a6 6 0 0 1-7.922-8.982 6 6 0 0 1 8.982 7.922l3.04 3.04a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215ZM11.5 7a4.499 4.499 0 1 0-8.997 0A4.499 4.499 0 0 0 11.5 7Z"></path> </svg> </span> <div data-target="query-builder.styledInputContainer" class="QueryBuilder-StyledInputContainer"> <div aria-hidden="true" class="QueryBuilder-StyledInputContent" data-target="query-builder.styledInputContent" ></div> <div class="QueryBuilder-InputWrapper"> <div aria-hidden="true" class="QueryBuilder-Sizer" data-target="query-builder.sizer"></div> <input id="query-builder-test" name="query-builder-test" value="" autocomplete="off" type="text" role="combobox" spellcheck="false" aria-expanded="false" aria-describedby="validation-b023ea82-6934-4f10-8d7a-d82509cb35bd" data-target="query-builder.input" data-action=" input:query-builder#inputChange blur:query-builder#inputBlur keydown:query-builder#inputKeydown focus:query-builder#inputFocus " data-view-component="true" class="FormControl-input QueryBuilder-Input FormControl-medium" /> </div> </div> <span class="sr-only" id="query-builder-test-clear">Clear</span> <button role="button" id="query-builder-test-clear-button" aria-labelledby="query-builder-test-clear query-builder-test-label" data-target="query-builder.clearButton" data-action=" click:query-builder#clear focus:query-builder#clearButtonFocus blur:query-builder#clearButtonBlur " variant="small" hidden="hidden" type="button" data-view-component="true" class="Button Button--iconOnly Button--invisible Button--medium mr-1 px-2 py-0 d-flex flex-items-center rounded-1 color-fg-muted"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x-circle-fill Button-visual"> <path d="M2.343 13.657A8 8 0 1 1 13.658 2.343 8 8 0 0 1 2.343 13.657ZM6.03 4.97a.751.751 0 0 0-1.042.018.751.751 0 0 0-.018 1.042L6.94 8 4.97 9.97a.749.749 0 0 0 .326 1.275.749.749 0 0 0 .734-.215L8 9.06l1.97 1.97a.749.749 0 0 0 1.275-.326.749.749 0 0 0-.215-.734L9.06 8l1.97-1.97a.749.749 0 0 0-.326-1.275.749.749 0 0 0-.734.215L8 6.94Z"></path> </svg> </button> </div> <template id="search-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-search"> <path d="M10.68 11.74a6 6 0 0 1-7.922-8.982 6 6 0 0 1 8.982 7.922l3.04 3.04a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215ZM11.5 7a4.499 4.499 0 1 0-8.997 0A4.499 4.499 0 0 0 11.5 7Z"></path> </svg> </template> <template id="code-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-code"> <path d="m11.28 3.22 4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734L13.94 8l-3.72-3.72a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215Zm-6.56 0a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042L2.06 8l3.72 3.72a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L.47 8.53a.75.75 0 0 1 0-1.06Z"></path> </svg> </template> <template id="file-code-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-file-code"> <path d="M4 1.75C4 .784 4.784 0 5.75 0h5.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v8.586A1.75 1.75 0 0 1 14.25 15h-9a.75.75 0 0 1 0-1.5h9a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 10 4.25V1.5H5.75a.25.25 0 0 0-.25.25v2.5a.75.75 0 0 1-1.5 0Zm1.72 4.97a.75.75 0 0 1 1.06 0l2 2a.75.75 0 0 1 0 1.06l-2 2a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734l1.47-1.47-1.47-1.47a.75.75 0 0 1 0-1.06ZM3.28 7.78 1.81 9.25l1.47 1.47a.751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018l-2-2a.75.75 0 0 1 0-1.06l2-2a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042Zm8.22-6.218V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path> </svg> </template> <template id="history-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-history"> <path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path> </svg> </template> <template id="repo-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo"> <path d="M2 2.5A2.5 2.5 0 0 1 4.5 0h8.75a.75.75 0 0 1 .75.75v12.5a.75.75 0 0 1-.75.75h-2.5a.75.75 0 0 1 0-1.5h1.75v-2h-8a1 1 0 0 0-.714 1.7.75.75 0 1 1-1.072 1.05A2.495 2.495 0 0 1 2 11.5Zm10.5-1h-8a1 1 0 0 0-1 1v6.708A2.486 2.486 0 0 1 4.5 9h8ZM5 12.25a.25.25 0 0 1 .25-.25h3.5a.25.25 0 0 1 .25.25v3.25a.25.25 0 0 1-.4.2l-1.45-1.087a.249.249 0 0 0-.3 0L5.4 15.7a.25.25 0 0 1-.4-.2Z"></path> </svg> </template> <template id="bookmark-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-bookmark"> <path d="M3 2.75C3 1.784 3.784 1 4.75 1h6.5c.966 0 1.75.784 1.75 1.75v11.5a.75.75 0 0 1-1.227.579L8 11.722l-3.773 3.107A.751.751 0 0 1 3 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.91l3.023-2.489a.75.75 0 0 1 .954 0l3.023 2.49V2.75a.25.25 0 0 0-.25-.25Z"></path> </svg> </template> <template id="plus-circle-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-plus-circle"> <path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Zm7.25-3.25v2.5h2.5a.75.75 0 0 1 0 1.5h-2.5v2.5a.75.75 0 0 1-1.5 0v-2.5h-2.5a.75.75 0 0 1 0-1.5h2.5v-2.5a.75.75 0 0 1 1.5 0Z"></path> </svg> </template> <template id="circle-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> </template> <template id="trash-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-trash"> <path d="M11 1.75V3h2.25a.75.75 0 0 1 0 1.5H2.75a.75.75 0 0 1 0-1.5H5V1.75C5 .784 5.784 0 6.75 0h2.5C10.216 0 11 .784 11 1.75ZM4.496 6.675l.66 6.6a.25.25 0 0 0 .249.225h5.19a.25.25 0 0 0 .249-.225l.66-6.6a.75.75 0 0 1 1.492.149l-.66 6.6A1.748 1.748 0 0 1 10.595 15h-5.19a1.75 1.75 0 0 1-1.741-1.575l-.66-6.6a.75.75 0 1 1 1.492-.15ZM6.5 1.75V3h3V1.75a.25.25 0 0 0-.25-.25h-2.5a.25.25 0 0 0-.25.25Z"></path> </svg> </template> <template id="team-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-people"> <path d="M2 5.5a3.5 3.5 0 1 1 5.898 2.549 5.508 5.508 0 0 1 3.034 4.084.75.75 0 1 1-1.482.235 4 4 0 0 0-7.9 0 .75.75 0 0 1-1.482-.236A5.507 5.507 0 0 1 3.102 8.05 3.493 3.493 0 0 1 2 5.5ZM11 4a3.001 3.001 0 0 1 2.22 5.018 5.01 5.01 0 0 1 2.56 3.012.749.749 0 0 1-.885.954.752.752 0 0 1-.549-.514 3.507 3.507 0 0 0-2.522-2.372.75.75 0 0 1-.574-.73v-.352a.75.75 0 0 1 .416-.672A1.5 1.5 0 0 0 11 5.5.75.75 0 0 1 11 4Zm-5.5-.5a2 2 0 1 0-.001 3.999A2 2 0 0 0 5.5 3.5Z"></path> </svg> </template> <template id="project-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-project"> <path d="M1.75 0h12.5C15.216 0 16 .784 16 1.75v12.5A1.75 1.75 0 0 1 14.25 16H1.75A1.75 1.75 0 0 1 0 14.25V1.75C0 .784.784 0 1.75 0ZM1.5 1.75v12.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25V1.75a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25ZM11.75 3a.75.75 0 0 1 .75.75v7.5a.75.75 0 0 1-1.5 0v-7.5a.75.75 0 0 1 .75-.75Zm-8.25.75a.75.75 0 0 1 1.5 0v5.5a.75.75 0 0 1-1.5 0ZM8 3a.75.75 0 0 1 .75.75v3.5a.75.75 0 0 1-1.5 0v-3.5A.75.75 0 0 1 8 3Z"></path> </svg> </template> <template id="pencil-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-pencil"> <path d="M11.013 1.427a1.75 1.75 0 0 1 2.474 0l1.086 1.086a1.75 1.75 0 0 1 0 2.474l-8.61 8.61c-.21.21-.47.364-.756.445l-3.251.93a.75.75 0 0 1-.927-.928l.929-3.25c.081-.286.235-.547.445-.758l8.61-8.61Zm.176 4.823L9.75 4.81l-6.286 6.287a.253.253 0 0 0-.064.108l-.558 1.953 1.953-.558a.253.253 0 0 0 .108-.064Zm1.238-3.763a.25.25 0 0 0-.354 0L10.811 3.75l1.439 1.44 1.263-1.263a.25.25 0 0 0 0-.354Z"></path> </svg> </template> <template id="copilot-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copilot"> <path d="M7.998 15.035c-4.562 0-7.873-2.914-7.998-3.749V9.338c.085-.628.677-1.686 1.588-2.065.013-.07.024-.143.036-.218.029-.183.06-.384.126-.612-.201-.508-.254-1.084-.254-1.656 0-.87.128-1.769.693-2.484.579-.733 1.494-1.124 2.724-1.261 1.206-.134 2.262.034 2.944.765.05.053.096.108.139.165.044-.057.094-.112.143-.165.682-.731 1.738-.899 2.944-.765 1.23.137 2.145.528 2.724 1.261.566.715.693 1.614.693 2.484 0 .572-.053 1.148-.254 1.656.066.228.098.429.126.612.012.076.024.148.037.218.924.385 1.522 1.471 1.591 2.095v1.872c0 .766-3.351 3.795-8.002 3.795Zm0-1.485c2.28 0 4.584-1.11 5.002-1.433V7.862l-.023-.116c-.49.21-1.075.291-1.727.291-1.146 0-2.059-.327-2.71-.991A3.222 3.222 0 0 1 8 6.303a3.24 3.24 0 0 1-.544.743c-.65.664-1.563.991-2.71.991-.652 0-1.236-.081-1.727-.291l-.023.116v4.255c.419.323 2.722 1.433 5.002 1.433ZM6.762 2.83c-.193-.206-.637-.413-1.682-.297-1.019.113-1.479.404-1.713.7-.247.312-.369.789-.369 1.554 0 .793.129 1.171.308 1.371.162.181.519.379 1.442.379.853 0 1.339-.235 1.638-.54.315-.322.527-.827.617-1.553.117-.935-.037-1.395-.241-1.614Zm4.155-.297c-1.044-.116-1.488.091-1.681.297-.204.219-.359.679-.242 1.614.091.726.303 1.231.618 1.553.299.305.784.54 1.638.54.922 0 1.28-.198 1.442-.379.179-.2.308-.578.308-1.371 0-.765-.123-1.242-.37-1.554-.233-.296-.693-.587-1.713-.7Z"></path><path d="M6.25 9.037a.75.75 0 0 1 .75.75v1.501a.75.75 0 0 1-1.5 0V9.787a.75.75 0 0 1 .75-.75Zm4.25.75v1.501a.75.75 0 0 1-1.5 0V9.787a.75.75 0 0 1 1.5 0Z"></path> </svg> </template> <template id="copilot-error-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copilot-error"> <path d="M16 11.24c0 .112-.072.274-.21.467L13 9.688V7.862l-.023-.116c-.49.21-1.075.291-1.727.291-.198 0-.388-.009-.571-.029L6.833 5.226a4.01 4.01 0 0 0 .17-.782c.117-.935-.037-1.395-.241-1.614-.193-.206-.637-.413-1.682-.297-.683.076-1.115.231-1.395.415l-1.257-.91c.579-.564 1.413-.877 2.485-.996 1.206-.134 2.262.034 2.944.765.05.053.096.108.139.165.044-.057.094-.112.143-.165.682-.731 1.738-.899 2.944-.765 1.23.137 2.145.528 2.724 1.261.566.715.693 1.614.693 2.484 0 .572-.053 1.148-.254 1.656.066.228.098.429.126.612.012.076.024.148.037.218.924.385 1.522 1.471 1.591 2.095Zm-5.083-8.707c-1.044-.116-1.488.091-1.681.297-.204.219-.359.679-.242 1.614.091.726.303 1.231.618 1.553.299.305.784.54 1.638.54.922 0 1.28-.198 1.442-.379.179-.2.308-.578.308-1.371 0-.765-.123-1.242-.37-1.554-.233-.296-.693-.587-1.713-.7Zm2.511 11.074c-1.393.776-3.272 1.428-5.43 1.428-4.562 0-7.873-2.914-7.998-3.749V9.338c.085-.628.677-1.686 1.588-2.065.013-.07.024-.143.036-.218.029-.183.06-.384.126-.612-.18-.455-.241-.963-.252-1.475L.31 4.107A.747.747 0 0 1 0 3.509V3.49a.748.748 0 0 1 .625-.73c.156-.026.306.047.435.139l14.667 10.578a.592.592 0 0 1 .227.264.752.752 0 0 1 .046.249v.022a.75.75 0 0 1-1.19.596Zm-1.367-.991L5.635 7.964a5.128 5.128 0 0 1-.889.073c-.652 0-1.236-.081-1.727-.291l-.023.116v4.255c.419.323 2.722 1.433 5.002 1.433 1.539 0 3.089-.505 4.063-.934Z"></path> </svg> </template> <template id="workflow-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-workflow"> <path d="M0 1.75C0 .784.784 0 1.75 0h3.5C6.216 0 7 .784 7 1.75v3.5A1.75 1.75 0 0 1 5.25 7H4v4a1 1 0 0 0 1 1h4v-1.25C9 9.784 9.784 9 10.75 9h3.5c.966 0 1.75.784 1.75 1.75v3.5A1.75 1.75 0 0 1 14.25 16h-3.5A1.75 1.75 0 0 1 9 14.25v-.75H5A2.5 2.5 0 0 1 2.5 11V7h-.75A1.75 1.75 0 0 1 0 5.25Zm1.75-.25a.25.25 0 0 0-.25.25v3.5c0 .138.112.25.25.25h3.5a.25.25 0 0 0 .25-.25v-3.5a.25.25 0 0 0-.25-.25Zm9 9a.25.25 0 0 0-.25.25v3.5c0 .138.112.25.25.25h3.5a.25.25 0 0 0 .25-.25v-3.5a.25.25 0 0 0-.25-.25Z"></path> </svg> </template> <template id="book-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-book"> <path d="M0 1.75A.75.75 0 0 1 .75 1h4.253c1.227 0 2.317.59 3 1.501A3.743 3.743 0 0 1 11.006 1h4.245a.75.75 0 0 1 .75.75v10.5a.75.75 0 0 1-.75.75h-4.507a2.25 2.25 0 0 0-1.591.659l-.622.621a.75.75 0 0 1-1.06 0l-.622-.621A2.25 2.25 0 0 0 5.258 13H.75a.75.75 0 0 1-.75-.75Zm7.251 10.324.004-5.073-.002-2.253A2.25 2.25 0 0 0 5.003 2.5H1.5v9h3.757a3.75 3.75 0 0 1 1.994.574ZM8.755 4.75l-.004 7.322a3.752 3.752 0 0 1 1.992-.572H14.5v-9h-3.495a2.25 2.25 0 0 0-2.25 2.25Z"></path> </svg> </template> <template id="code-review-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-code-review"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v8.5A1.75 1.75 0 0 1 14.25 13H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25v-8.5C0 1.784.784 1 1.75 1ZM1.5 2.75v8.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-8.5a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25Zm5.28 1.72a.75.75 0 0 1 0 1.06L5.31 7l1.47 1.47a.751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018l-2-2a.75.75 0 0 1 0-1.06l2-2a.75.75 0 0 1 1.06 0Zm2.44 0a.75.75 0 0 1 1.06 0l2 2a.75.75 0 0 1 0 1.06l-2 2a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L10.69 7 9.22 5.53a.75.75 0 0 1 0-1.06Z"></path> </svg> </template> <template id="codespaces-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-codespaces"> <path d="M0 11.25c0-.966.784-1.75 1.75-1.75h12.5c.966 0 1.75.784 1.75 1.75v3A1.75 1.75 0 0 1 14.25 16H1.75A1.75 1.75 0 0 1 0 14.25Zm2-9.5C2 .784 2.784 0 3.75 0h8.5C13.216 0 14 .784 14 1.75v5a1.75 1.75 0 0 1-1.75 1.75h-8.5A1.75 1.75 0 0 1 2 6.75Zm1.75-.25a.25.25 0 0 0-.25.25v5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-5a.25.25 0 0 0-.25-.25Zm-2 9.5a.25.25 0 0 0-.25.25v3c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25v-3a.25.25 0 0 0-.25-.25Z"></path><path d="M7 12.75a.75.75 0 0 1 .75-.75h4.5a.75.75 0 0 1 0 1.5h-4.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> </template> <template id="comment-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-comment"> <path d="M1 2.75C1 1.784 1.784 1 2.75 1h10.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 13.25 12H9.06l-2.573 2.573A1.458 1.458 0 0 1 4 13.543V12H2.75A1.75 1.75 0 0 1 1 10.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h4.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> </template> <template id="comment-discussion-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-comment-discussion"> <path d="M1.75 1h8.5c.966 0 1.75.784 1.75 1.75v5.5A1.75 1.75 0 0 1 10.25 10H7.061l-2.574 2.573A1.458 1.458 0 0 1 2 11.543V10h-.25A1.75 1.75 0 0 1 0 8.25v-5.5C0 1.784.784 1 1.75 1ZM1.5 2.75v5.5c0 .138.112.25.25.25h1a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h3.5a.25.25 0 0 0 .25-.25v-5.5a.25.25 0 0 0-.25-.25h-8.5a.25.25 0 0 0-.25.25Zm13 2a.25.25 0 0 0-.25-.25h-.5a.75.75 0 0 1 0-1.5h.5c.966 0 1.75.784 1.75 1.75v5.5A1.75 1.75 0 0 1 14.25 12H14v1.543a1.458 1.458 0 0 1-2.487 1.03L9.22 12.28a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215l2.22 2.22v-2.19a.75.75 0 0 1 .75-.75h1a.25.25 0 0 0 .25-.25Z"></path> </svg> </template> <template id="organization-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-organization"> <path d="M1.75 16A1.75 1.75 0 0 1 0 14.25V1.75C0 .784.784 0 1.75 0h8.5C11.216 0 12 .784 12 1.75v12.5c0 .085-.006.168-.018.25h2.268a.25.25 0 0 0 .25-.25V8.285a.25.25 0 0 0-.111-.208l-1.055-.703a.749.749 0 1 1 .832-1.248l1.055.703c.487.325.779.871.779 1.456v5.965A1.75 1.75 0 0 1 14.25 16h-3.5a.766.766 0 0 1-.197-.026c-.099.017-.2.026-.303.026h-3a.75.75 0 0 1-.75-.75V14h-1v1.25a.75.75 0 0 1-.75.75Zm-.25-1.75c0 .138.112.25.25.25H4v-1.25a.75.75 0 0 1 .75-.75h2.5a.75.75 0 0 1 .75.75v1.25h2.25a.25.25 0 0 0 .25-.25V1.75a.25.25 0 0 0-.25-.25h-8.5a.25.25 0 0 0-.25.25ZM3.75 6h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1 0-1.5ZM3 3.75A.75.75 0 0 1 3.75 3h.5a.75.75 0 0 1 0 1.5h-.5A.75.75 0 0 1 3 3.75Zm4 3A.75.75 0 0 1 7.75 6h.5a.75.75 0 0 1 0 1.5h-.5A.75.75 0 0 1 7 6.75ZM7.75 3h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1 0-1.5ZM3 9.75A.75.75 0 0 1 3.75 9h.5a.75.75 0 0 1 0 1.5h-.5A.75.75 0 0 1 3 9.75ZM7.75 9h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1 0-1.5Z"></path> </svg> </template> <template id="rocket-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-rocket"> <path d="M14.064 0h.186C15.216 0 16 .784 16 1.75v.186a8.752 8.752 0 0 1-2.564 6.186l-.458.459c-.314.314-.641.616-.979.904v3.207c0 .608-.315 1.172-.833 1.49l-2.774 1.707a.749.749 0 0 1-1.11-.418l-.954-3.102a1.214 1.214 0 0 1-.145-.125L3.754 9.816a1.218 1.218 0 0 1-.124-.145L.528 8.717a.749.749 0 0 1-.418-1.11l1.71-2.774A1.748 1.748 0 0 1 3.31 4h3.204c.288-.338.59-.665.904-.979l.459-.458A8.749 8.749 0 0 1 14.064 0ZM8.938 3.623h-.002l-.458.458c-.76.76-1.437 1.598-2.02 2.5l-1.5 2.317 2.143 2.143 2.317-1.5c.902-.583 1.74-1.26 2.499-2.02l.459-.458a7.25 7.25 0 0 0 2.123-5.127V1.75a.25.25 0 0 0-.25-.25h-.186a7.249 7.249 0 0 0-5.125 2.123ZM3.56 14.56c-.732.732-2.334 1.045-3.005 1.148a.234.234 0 0 1-.201-.064.234.234 0 0 1-.064-.201c.103-.671.416-2.273 1.15-3.003a1.502 1.502 0 1 1 2.12 2.12Zm6.94-3.935c-.088.06-.177.118-.266.175l-2.35 1.521.548 1.783 1.949-1.2a.25.25 0 0 0 .119-.213ZM3.678 8.116 5.2 5.766c.058-.09.117-.178.176-.266H3.309a.25.25 0 0 0-.213.119l-1.2 1.95ZM12 5a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> </template> <template id="shield-check-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-shield-check"> <path d="m8.533.133 5.25 1.68A1.75 1.75 0 0 1 15 3.48V7c0 1.566-.32 3.182-1.303 4.682-.983 1.498-2.585 2.813-5.032 3.855a1.697 1.697 0 0 1-1.33 0c-2.447-1.042-4.049-2.357-5.032-3.855C1.32 10.182 1 8.566 1 7V3.48a1.75 1.75 0 0 1 1.217-1.667l5.25-1.68a1.748 1.748 0 0 1 1.066 0Zm-.61 1.429.001.001-5.25 1.68a.251.251 0 0 0-.174.237V7c0 1.36.275 2.666 1.057 3.859.784 1.194 2.121 2.342 4.366 3.298a.196.196 0 0 0 .154 0c2.245-.957 3.582-2.103 4.366-3.297C13.225 9.666 13.5 8.358 13.5 7V3.48a.25.25 0 0 0-.174-.238l-5.25-1.68a.25.25 0 0 0-.153 0ZM11.28 6.28l-3.5 3.5a.75.75 0 0 1-1.06 0l-1.5-1.5a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215l.97.97 2.97-2.97a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042Z"></path> </svg> </template> <template id="heart-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-heart"> <path d="m8 14.25.345.666a.75.75 0 0 1-.69 0l-.008-.004-.018-.01a7.152 7.152 0 0 1-.31-.17 22.055 22.055 0 0 1-3.434-2.414C2.045 10.731 0 8.35 0 5.5 0 2.836 2.086 1 4.25 1 5.797 1 7.153 1.802 8 3.02 8.847 1.802 10.203 1 11.75 1 13.914 1 16 2.836 16 5.5c0 2.85-2.045 5.231-3.885 6.818a22.066 22.066 0 0 1-3.744 2.584l-.018.01-.006.003h-.002ZM4.25 2.5c-1.336 0-2.75 1.164-2.75 3 0 2.15 1.58 4.144 3.365 5.682A20.58 20.58 0 0 0 8 13.393a20.58 20.58 0 0 0 3.135-2.211C12.92 9.644 14.5 7.65 14.5 5.5c0-1.836-1.414-3-2.75-3-1.373 0-2.609.986-3.029 2.456a.749.749 0 0 1-1.442 0C6.859 3.486 5.623 2.5 4.25 2.5Z"></path> </svg> </template> <template id="server-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-server"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v4c0 .372-.116.717-.314 1 .198.283.314.628.314 1v4a1.75 1.75 0 0 1-1.75 1.75H1.75A1.75 1.75 0 0 1 0 12.75v-4c0-.358.109-.707.314-1a1.739 1.739 0 0 1-.314-1v-4C0 1.784.784 1 1.75 1ZM1.5 2.75v4c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25Zm.25 5.75a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25ZM7 4.75A.75.75 0 0 1 7.75 4h4.5a.75.75 0 0 1 0 1.5h-4.5A.75.75 0 0 1 7 4.75ZM7.75 10h4.5a.75.75 0 0 1 0 1.5h-4.5a.75.75 0 0 1 0-1.5ZM3 4.75A.75.75 0 0 1 3.75 4h.5a.75.75 0 0 1 0 1.5h-.5A.75.75 0 0 1 3 4.75ZM3.75 10h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1 0-1.5Z"></path> </svg> </template> <template id="globe-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-globe"> <path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM5.78 8.75a9.64 9.64 0 0 0 1.363 4.177c.255.426.542.832.857 1.215.245-.296.551-.705.857-1.215A9.64 9.64 0 0 0 10.22 8.75Zm4.44-1.5a9.64 9.64 0 0 0-1.363-4.177c-.307-.51-.612-.919-.857-1.215a9.927 9.927 0 0 0-.857 1.215A9.64 9.64 0 0 0 5.78 7.25Zm-5.944 1.5H1.543a6.507 6.507 0 0 0 4.666 5.5c-.123-.181-.24-.365-.352-.552-.715-1.192-1.437-2.874-1.581-4.948Zm-2.733-1.5h2.733c.144-2.074.866-3.756 1.58-4.948.12-.197.237-.381.353-.552a6.507 6.507 0 0 0-4.666 5.5Zm10.181 1.5c-.144 2.074-.866 3.756-1.58 4.948-.12.197-.237.381-.353.552a6.507 6.507 0 0 0 4.666-5.5Zm2.733-1.5a6.507 6.507 0 0 0-4.666-5.5c.123.181.24.365.353.552.714 1.192 1.436 2.874 1.58 4.948Z"></path> </svg> </template> <template id="issue-opened-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-issue-opened"> <path d="M8 9.5a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path><path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Z"></path> </svg> </template> <template id="device-mobile-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-device-mobile"> <path d="M3.75 0h8.5C13.216 0 14 .784 14 1.75v12.5A1.75 1.75 0 0 1 12.25 16h-8.5A1.75 1.75 0 0 1 2 14.25V1.75C2 .784 2.784 0 3.75 0ZM3.5 1.75v12.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25V1.75a.25.25 0 0 0-.25-.25h-8.5a.25.25 0 0 0-.25.25ZM8 13a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path> </svg> </template> <template id="package-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-package"> <path d="m8.878.392 5.25 3.045c.54.314.872.89.872 1.514v6.098a1.75 1.75 0 0 1-.872 1.514l-5.25 3.045a1.75 1.75 0 0 1-1.756 0l-5.25-3.045A1.75 1.75 0 0 1 1 11.049V4.951c0-.624.332-1.201.872-1.514L7.122.392a1.75 1.75 0 0 1 1.756 0ZM7.875 1.69l-4.63 2.685L8 7.133l4.755-2.758-4.63-2.685a.248.248 0 0 0-.25 0ZM2.5 5.677v5.372c0 .09.047.171.125.216l4.625 2.683V8.432Zm6.25 8.271 4.625-2.683a.25.25 0 0 0 .125-.216V5.677L8.75 8.432Z"></path> </svg> </template> <template id="credit-card-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-credit-card"> <path d="M10.75 9a.75.75 0 0 0 0 1.5h1.5a.75.75 0 0 0 0-1.5h-1.5Z"></path><path d="M0 3.75C0 2.784.784 2 1.75 2h12.5c.966 0 1.75.784 1.75 1.75v8.5A1.75 1.75 0 0 1 14.25 14H1.75A1.75 1.75 0 0 1 0 12.25ZM14.5 6.5h-13v5.75c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25Zm0-2.75a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25V5h13Z"></path> </svg> </template> <template id="play-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-play"> <path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Zm4.879-2.773 4.264 2.559a.25.25 0 0 1 0 .428l-4.264 2.559A.25.25 0 0 1 6 10.559V5.442a.25.25 0 0 1 .379-.215Z"></path> </svg> </template> <template id="gift-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-gift"> <path d="M2 2.75A2.75 2.75 0 0 1 4.75 0c.983 0 1.873.42 2.57 1.232.268.318.497.668.68 1.042.183-.375.411-.725.68-1.044C9.376.42 10.266 0 11.25 0a2.75 2.75 0 0 1 2.45 4h.55c.966 0 1.75.784 1.75 1.75v2c0 .698-.409 1.301-1 1.582v4.918A1.75 1.75 0 0 1 13.25 16H2.75A1.75 1.75 0 0 1 1 14.25V9.332C.409 9.05 0 8.448 0 7.75v-2C0 4.784.784 4 1.75 4h.55c-.192-.375-.3-.8-.3-1.25ZM7.25 9.5H2.5v4.75c0 .138.112.25.25.25h4.5Zm1.5 0v5h4.5a.25.25 0 0 0 .25-.25V9.5Zm0-4V8h5.5a.25.25 0 0 0 .25-.25v-2a.25.25 0 0 0-.25-.25Zm-7 0a.25.25 0 0 0-.25.25v2c0 .138.112.25.25.25h5.5V5.5h-5.5Zm3-4a1.25 1.25 0 0 0 0 2.5h2.309c-.233-.818-.542-1.401-.878-1.793-.43-.502-.915-.707-1.431-.707ZM8.941 4h2.309a1.25 1.25 0 0 0 0-2.5c-.516 0-1 .205-1.43.707-.337.392-.646.975-.879 1.793Z"></path> </svg> </template> <template id="code-square-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-code-square"> <path d="M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v12.5A1.75 1.75 0 0 1 14.25 16H1.75A1.75 1.75 0 0 1 0 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25V1.75a.25.25 0 0 0-.25-.25Zm7.47 3.97a.75.75 0 0 1 1.06 0l2 2a.75.75 0 0 1 0 1.06l-2 2a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734L10.69 8 9.22 6.53a.75.75 0 0 1 0-1.06ZM6.78 6.53 5.31 8l1.47 1.47a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215l-2-2a.75.75 0 0 1 0-1.06l2-2a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042Z"></path> </svg> </template> <template id="device-desktop-icon"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-device-desktop"> <path d="M14.25 1c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 14.25 12h-3.727c.099 1.041.52 1.872 1.292 2.757A.752.752 0 0 1 11.25 16h-6.5a.75.75 0 0 1-.565-1.243c.772-.885 1.192-1.716 1.292-2.757H1.75A1.75 1.75 0 0 1 0 10.25v-7.5C0 1.784.784 1 1.75 1ZM1.75 2.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25ZM9.018 12H6.982a5.72 5.72 0 0 1-.765 2.5h3.566a5.72 5.72 0 0 1-.765-2.5Z"></path> </svg> </template> <div class="position-relative"> <ul role="listbox" class="ActionListWrap QueryBuilder-ListWrap" aria-label="Suggestions" data-action=" combobox-commit:query-builder#comboboxCommit mousedown:query-builder#resultsMousedown " data-target="query-builder.resultsList" data-persist-list=false id="query-builder-test-results" ></ul> </div> <div class="FormControl-inlineValidation" id="validation-b023ea82-6934-4f10-8d7a-d82509cb35bd" hidden="hidden"> <span class="FormControl-inlineValidation--visual"> <svg aria-hidden="true" height="12" viewBox="0 0 12 12" version="1.1" width="12" data-view-component="true" class="octicon octicon-alert-fill"> <path d="M4.855.708c.5-.896 1.79-.896 2.29 0l4.675 8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path> </svg> </span> <span></span> </div> </div> <div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div> </query-builder></form> <div class="d-flex flex-row color-fg-muted px-3 text-small color-bg-default search-feedback-prompt"> <a target="_blank" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2">Search syntax tips</a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="feedback-dialog-title"> <div data-view-component="true" class="Overlay-body"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="code-search-feedback-form" data-turbo="false" action="/search/feedback" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="EOFcdnDbOcL4auLQIGsRQuT3a/4PAx+g6g4mYRMT4HzQdTdxgZtbhTzxL7Ib0R6Y+nl6GiqGKAyPQBC2HOH1cQ==" /> <p>We read every piece of feedback, and take your input very seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="61Ys5n8spuV4jiXfnWnMm+oorqOVfsooBN70rlSb0wkbNxSzTO/yOW7p0VXjsB8dtoxxAaDGtGW+I7pOsm3UAw==" /> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <input type="hidden" id="custom_scope_id" name="custom_scope_id" data-target="custom-scopes.customScopesIdField"> <div class="form-group"> <label for="custom_scope_name">Name</label> <auto-check src="/search/custom_scopes/check_name" required only-validate-on-blur="false"> <input type="text" name="custom_scope_name" id="custom_scope_name" data-target="custom-scopes.customScopesNameField" class="form-control" autocomplete="off" placeholder="github-ruby" required maxlength="50"> <input type="hidden" data-csrf="true" value="XKvEFQ9TKoXona7GDU6NJ+iNXzA5UTAFR/lHQVIApyuBZoGj4rtik7AAOWMu22MPASlrB2M9I88wHH4eGYBhSw==" /> </auto-check> </div> <div class="form-group"> <label for="custom_scope_query">Query</label> <input type="text" name="custom_scope_query" id="custom_scope_query" data-target="custom-scopes.customScopesQueryField" class="form-control" autocomplete="off" placeholder="(repo:mona/a OR repo:mona/b) AND lang:python" required maxlength="500"> </div> <p class="text-small color-fg-muted"> To see all available qualifiers, see our <a class="Link--inTextBlock" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax">documentation</a>. </p> </form> </div> <div data-target="custom-scopes.manageCustomScopesForm"> <div data-target="custom-scopes.list"></div> </div> </div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd Overlay-footer--divided"> <button data-action="click:custom-scopes#customScopesCancel" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="custom-scopes-dialog-form" data-action="click:custom-scopes#customScopesSubmit" data-target="custom-scopes.customScopesSubmitButton" type="submit" data-view-component="true" class="btn-primary btn"> Create saved search </button> </div> </dialog></dialog-helper> </custom-scopes> </div> </qbsearch-input> <div class="position-relative HeaderMenu-link-wrap d-lg-inline-block"> <a href="/login?return_to=https%3A%2F%2Fgithub.com%2Fbenedekrozemberczki%2Fawesome-gradient-boosting-papers" class="HeaderMenu-link HeaderMenu-link--sign-in HeaderMenu-button flex-shrink-0 no-underline d-none d-lg-inline-flex border border-lg-0 rounded rounded-lg-0 px-2 py-1" style="margin-left: 12px;" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"site header menu","repository_id":null,"auth_type":"SIGN_UP","originating_url":"https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers","user_id":null}}" data-hydro-click-hmac="89ebe38c4ba8405160fc724ae57008a8c3ac3c9cbb25eb46ad707403f3ae6a7c" data-analytics-event="{"category":"Marketing nav","action":"click to go to homepage","label":"ref_page:Marketing;ref_cta:Sign in;ref_loc:Header"}" > Sign in </a> </div> <a href="/signup?ref_cta=Sign+up&ref_loc=header+logged+out&ref_page=%2F%3Cuser-name%3E%2F%3Crepo-name%3E&source=header-repo&source_repo=benedekrozemberczki%2Fawesome-gradient-boosting-papers" class="HeaderMenu-link HeaderMenu-link--sign-up HeaderMenu-button flex-shrink-0 d-flex d-lg-inline-flex no-underline border color-border-default rounded px-2 py-1" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"site header menu","repository_id":null,"auth_type":"SIGN_UP","originating_url":"https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers","user_id":null}}" data-hydro-click-hmac="89ebe38c4ba8405160fc724ae57008a8c3ac3c9cbb25eb46ad707403f3ae6a7c" data-analytics-event="{"category":"Sign up","action":"click to sign up for account","label":"ref_page:/<user-name>/<repo-name>;ref_cta:Sign up;ref_loc:header logged out"}" > Sign up </a> <button type="button" class="sr-only js-header-menu-focus-trap d-block d-lg-none">Reseting focus</button> </div> </div> </div> </div> </header> <div hidden="hidden" data-view-component="true" class="js-stale-session-flash stale-session-flash flash flash-warn flash-full"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-alert"> <path d="M6.457 1.047c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0 1 14.082 15H1.918a1.75 1.75 0 0 1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <span class="js-stale-session-flash-signed-in" hidden>You signed in with another tab or window. <a class="Link--inTextBlock" href="">Reload</a> to refresh your session.</span> <span class="js-stale-session-flash-signed-out" hidden>You signed out in another tab or window. <a class="Link--inTextBlock" href="">Reload</a> to refresh your session.</span> <span class="js-stale-session-flash-switched" hidden>You switched accounts on another tab or window. <a class="Link--inTextBlock" href="">Reload</a> to refresh your session.</span> <button id="icon-button-eafc9eb4-d60c-4554-82d2-614e09b76464" aria-labelledby="tooltip-1dc70939-aa61-40d8-b86f-0bb5abaa4bc6" type="button" data-view-component="true" class="Button Button--iconOnly Button--invisible Button--medium flash-close js-flash-close"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x Button-visual"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button><tool-tip id="tooltip-1dc70939-aa61-40d8-b86f-0bb5abaa4bc6" for="icon-button-eafc9eb4-d60c-4554-82d2-614e09b76464" popover="manual" data-direction="s" data-type="label" data-view-component="true" class="sr-only position-absolute">Dismiss alert</tool-tip> </div> </div> <div id="start-of-content" class="show-on-focus"></div> <div id="js-flash-container" class="flash-container" data-turbo-replace> <template class="js-flash-template"> <div class="flash flash-full {{ className }}"> <div > <button autofocus class="flash-close js-flash-close" type="button" aria-label="Dismiss this message"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div aria-atomic="true" role="alert" class="js-flash-alert"> <div>{{ message }}</div> </div> </div> </div> </template> </div> <div class="application-main " data-commit-hovercards-enabled data-discussion-hovercards-enabled data-issue-and-pr-hovercards-enabled data-project-hovercards-enabled > <div itemscope itemtype="http://schema.org/SoftwareSourceCode" class=""> <main id="js-repo-pjax-container" > <div id="repository-container-header" class="pt-3 hide-full-screen" style="background-color: var(--page-header-bgColor, var(--color-page-header-bg));" data-turbo-replace> <div class="d-flex flex-nowrap flex-justify-end mb-3 px-3 px-lg-5" style="gap: 1rem;"> <div class="flex-auto min-width-0 width-fit"> <div class=" d-flex flex-wrap flex-items-center wb-break-word f3 text-normal"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo color-fg-muted mr-2"> <path d="M2 2.5A2.5 2.5 0 0 1 4.5 0h8.75a.75.75 0 0 1 .75.75v12.5a.75.75 0 0 1-.75.75h-2.5a.75.75 0 0 1 0-1.5h1.75v-2h-8a1 1 0 0 0-.714 1.7.75.75 0 1 1-1.072 1.05A2.495 2.495 0 0 1 2 11.5Zm10.5-1h-8a1 1 0 0 0-1 1v6.708A2.486 2.486 0 0 1 4.5 9h8ZM5 12.25a.25.25 0 0 1 .25-.25h3.5a.25.25 0 0 1 .25.25v3.25a.25.25 0 0 1-.4.2l-1.45-1.087a.249.249 0 0 0-.3 0L5.4 15.7a.25.25 0 0 1-.4-.2Z"></path> </svg> <span class="author flex-self-stretch" itemprop="author"> <a class="url fn" rel="author" data-hovercard-type="user" data-hovercard-url="/users/benedekrozemberczki/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="/benedekrozemberczki"> benedekrozemberczki </a> </span> <span class="mx-1 flex-self-stretch color-fg-muted">/</span> <strong itemprop="name" class="mr-2 flex-self-stretch"> <a data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/benedekrozemberczki/awesome-gradient-boosting-papers">awesome-gradient-boosting-papers</a> </strong> <span></span><span class="Label Label--secondary v-align-middle mr-1">Public</span> </div> </div> <div id="repository-details-container" class="flex-shrink-0" data-turbo-replace style="max-width: 70%;"> <ul class="pagehead-actions flex-shrink-0 d-none d-md-inline" style="padding: 2px 0;"> <li> <include-fragment src="/benedekrozemberczki/awesome-gradient-boosting-papers/sponsor_button"></include-fragment> </li> <li> <a href="/login?return_to=%2Fbenedekrozemberczki%2Fawesome-gradient-boosting-papers" rel="nofollow" id="repository-details-watch-button" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"notification subscription menu watch","repository_id":null,"auth_type":"LOG_IN","originating_url":"https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers","user_id":null}}" data-hydro-click-hmac="9f2113a130cd8b677dc4d3e9a2188b8c2eb344682c7d1886f8d387f889233f9c" aria-label="You must be signed in to change notification settings" data-view-component="true" class="btn-sm btn"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-bell mr-2"> <path d="M8 16a2 2 0 0 0 1.985-1.75c.017-.137-.097-.25-.235-.25h-3.5c-.138 0-.252.113-.235.25A2 2 0 0 0 8 16ZM3 5a5 5 0 0 1 10 0v2.947c0 .05.015.098.042.139l1.703 2.555A1.519 1.519 0 0 1 13.482 13H2.518a1.516 1.516 0 0 1-1.263-2.36l1.703-2.554A.255.255 0 0 0 3 7.947Zm5-3.5A3.5 3.5 0 0 0 4.5 5v2.947c0 .346-.102.683-.294.97l-1.703 2.556a.017.017 0 0 0-.003.01l.001.006c0 .002.002.004.004.006l.006.004.007.001h10.964l.007-.001.006-.004.004-.006.001-.007a.017.017 0 0 0-.003-.01l-1.703-2.554a1.745 1.745 0 0 1-.294-.97V5A3.5 3.5 0 0 0 8 1.5Z"></path> </svg>Notifications </a> <tool-tip id="tooltip-cb8c831d-a1cf-40ca-a649-ad9cdda9b576" for="repository-details-watch-button" popover="manual" data-direction="s" data-type="description" data-view-component="true" class="sr-only position-absolute">You must be signed in to change notification settings</tool-tip> </li> <li> <a icon="repo-forked" id="fork-button" href="/login?return_to=%2Fbenedekrozemberczki%2Fawesome-gradient-boosting-papers" rel="nofollow" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"repo details fork button","repository_id":186163475,"auth_type":"LOG_IN","originating_url":"https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers","user_id":null}}" data-hydro-click-hmac="9f838b604678fdde55ee4c5dcd797900844307be3e6b2ad03238880cf064a45d" data-view-component="true" class="btn-sm btn"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg>Fork <span id="repo-network-counter" data-pjax-replace="true" data-turbo-replace="true" title="158" data-view-component="true" class="Counter">158</span> </a> </li> <li> <div data-view-component="true" class="BtnGroup d-flex"> <a href="/login?return_to=%2Fbenedekrozemberczki%2Fawesome-gradient-boosting-papers" rel="nofollow" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"star button","repository_id":186163475,"auth_type":"LOG_IN","originating_url":"https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers","user_id":null}}" data-hydro-click-hmac="7797c7d1a0dadbdeaab30cf143c13130b244ba999de3672082d808227081a21f" aria-label="You must be signed in to star a repository" data-view-component="true" class="tooltipped tooltipped-sw btn-sm btn"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star v-align-text-bottom d-inline-block mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg><span data-view-component="true" class="d-inline"> Star </span> <span id="repo-stars-counter-star" aria-label="1019 users starred this repository" data-singular-suffix="user starred this repository" data-plural-suffix="users starred this repository" data-turbo-replace="true" title="1,019" data-view-component="true" class="Counter js-social-count">1k</span> </a></div> </li> </ul> </div> </div> <div id="responsive-meta-container" data-turbo-replace> <div class="d-block d-md-none mb-2 px-3 px-md-4 px-lg-5"> <p class="f4 mb-3 "> A curated list of gradient boosting research papers with implementations. </p> <h3 class="sr-only">License</h3> <div class="mb-2"> <a href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/LICENSE" class="Link--muted" data-analytics-event="{"category":"Repository Overview","action":"click","label":"location:sidebar;file:license"}" > <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-law mr-2"> <path d="M8.75.75V2h.985c.304 0 .603.08.867.231l1.29.736c.038.022.08.033.124.033h2.234a.75.75 0 0 1 0 1.5h-.427l2.111 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.006.005-.01.01-.045.04c-.21.176-.441.327-.686.45C14.556 10.78 13.88 11 13 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L12.178 4.5h-.162c-.305 0-.604-.079-.868-.231l-1.29-.736a.245.245 0 0 0-.124-.033H8.75V13h2.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1 0-1.5h2.5V3.5h-.984a.245.245 0 0 0-.124.033l-1.289.737c-.265.15-.564.23-.869.23h-.162l2.112 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.016.015-.045.04c-.21.176-.441.327-.686.45C4.556 10.78 3.88 11 3 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L2.178 4.5H1.75a.75.75 0 0 1 0-1.5h2.234a.249.249 0 0 0 .125-.033l1.288-.737c.265-.15.564-.23.869-.23h.984V.75a.75.75 0 0 1 1.5 0Zm2.945 8.477c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L13 6.327Zm-10 0c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L3 6.327Z"></path> </svg> CC0-1.0 license </a> </div> <div class="mb-3"> <a class="Link--secondary no-underline mr-3" href="/benedekrozemberczki/awesome-gradient-boosting-papers/stargazers"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star mr-1"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <span class="text-bold">1k</span> stars </a> <a class="Link--secondary no-underline mr-3" href="/benedekrozemberczki/awesome-gradient-boosting-papers/forks"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-1"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <span class="text-bold">158</span> forks </a> <a class="Link--secondary no-underline mr-3 d-inline-block" href="/benedekrozemberczki/awesome-gradient-boosting-papers/branches"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-git-branch mr-1"> <path d="M9.5 3.25a2.25 2.25 0 1 1 3 2.122V6A2.5 2.5 0 0 1 10 8.5H6a1 1 0 0 0-1 1v1.128a2.251 2.251 0 1 1-1.5 0V5.372a2.25 2.25 0 1 1 1.5 0v1.836A2.493 2.493 0 0 1 6 7h4a1 1 0 0 0 1-1v-.628A2.25 2.25 0 0 1 9.5 3.25Zm-6 0a.75.75 0 1 0 1.5 0 .75.75 0 0 0-1.5 0Zm8.25-.75a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5ZM4.25 12a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5Z"></path> </svg> <span>Branches</span> </a> <a class="Link--secondary no-underline d-inline-block" href="/benedekrozemberczki/awesome-gradient-boosting-papers/tags"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag mr-1"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <span>Tags</span> </a> <a class="Link--secondary no-underline d-inline-block" href="/benedekrozemberczki/awesome-gradient-boosting-papers/activity"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-pulse mr-1"> <path d="M6 2c.306 0 .582.187.696.471L10 10.731l1.304-3.26A.751.751 0 0 1 12 7h3.25a.75.75 0 0 1 0 1.5h-2.742l-1.812 4.528a.751.751 0 0 1-1.392 0L6 4.77 4.696 8.03A.75.75 0 0 1 4 8.5H.75a.75.75 0 0 1 0-1.5h2.742l1.812-4.529A.751.751 0 0 1 6 2Z"></path> </svg> <span>Activity</span> </a> </div> <div class="d-flex flex-wrap gap-2"> <div class="flex-1"> <div data-view-component="true" class="BtnGroup d-flex"> <a href="/login?return_to=%2Fbenedekrozemberczki%2Fawesome-gradient-boosting-papers" rel="nofollow" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"star button","repository_id":186163475,"auth_type":"LOG_IN","originating_url":"https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers","user_id":null}}" data-hydro-click-hmac="7797c7d1a0dadbdeaab30cf143c13130b244ba999de3672082d808227081a21f" aria-label="You must be signed in to star a repository" data-view-component="true" class="tooltipped tooltipped-sw btn-sm btn btn-block"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star v-align-text-bottom d-inline-block mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg><span data-view-component="true" class="d-inline"> Star </span> </a></div> </div> <div class="flex-1"> <a href="/login?return_to=%2Fbenedekrozemberczki%2Fawesome-gradient-boosting-papers" rel="nofollow" id="files-overview-watch-button" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"notification subscription menu watch","repository_id":null,"auth_type":"LOG_IN","originating_url":"https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers","user_id":null}}" data-hydro-click-hmac="9f2113a130cd8b677dc4d3e9a2188b8c2eb344682c7d1886f8d387f889233f9c" aria-label="You must be signed in to change notification settings" data-view-component="true" class="btn-sm btn btn-block"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-bell mr-2"> <path d="M8 16a2 2 0 0 0 1.985-1.75c.017-.137-.097-.25-.235-.25h-3.5c-.138 0-.252.113-.235.25A2 2 0 0 0 8 16ZM3 5a5 5 0 0 1 10 0v2.947c0 .05.015.098.042.139l1.703 2.555A1.519 1.519 0 0 1 13.482 13H2.518a1.516 1.516 0 0 1-1.263-2.36l1.703-2.554A.255.255 0 0 0 3 7.947Zm5-3.5A3.5 3.5 0 0 0 4.5 5v2.947c0 .346-.102.683-.294.97l-1.703 2.556a.017.017 0 0 0-.003.01l.001.006c0 .002.002.004.004.006l.006.004.007.001h10.964l.007-.001.006-.004.004-.006.001-.007a.017.017 0 0 0-.003-.01l-1.703-2.554a1.745 1.745 0 0 1-.294-.97V5A3.5 3.5 0 0 0 8 1.5Z"></path> </svg>Notifications </a> <tool-tip id="tooltip-c59b03b1-5f3c-4f6e-bc7a-93b8811e8d6e" for="files-overview-watch-button" popover="manual" data-direction="s" data-type="description" data-view-component="true" class="sr-only position-absolute">You must be signed in to change notification settings</tool-tip> </div> <span> </span> </div> </div> </div> <nav data-pjax="#js-repo-pjax-container" aria-label="Repository" data-view-component="true" class="js-repo-nav js-sidenav-container-pjax js-responsive-underlinenav overflow-hidden UnderlineNav px-3 px-md-4 px-lg-5"> <ul data-view-component="true" class="UnderlineNav-body list-style-none"> <li data-view-component="true" class="d-inline-flex"> <a id="code-tab" href="/benedekrozemberczki/awesome-gradient-boosting-papers" data-tab-item="i0code-tab" data-selected-links="repo_source repo_downloads repo_commits repo_releases repo_tags repo_branches repo_packages repo_deployments repo_attestations /benedekrozemberczki/awesome-gradient-boosting-papers" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-hotkey="g c" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Code","target":"UNDERLINE_NAV.TAB"}" aria-current="page" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navigation-item selected"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-code UnderlineNav-octicon d-none d-sm-inline"> <path d="m11.28 3.22 4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734L13.94 8l-3.72-3.72a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215Zm-6.56 0a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042L2.06 8l3.72 3.72a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L.47 8.53a.75.75 0 0 1 0-1.06Z"></path> </svg> <span data-content="Code">Code</span> <span id="code-repo-tab-count" data-pjax-replace="" data-turbo-replace="" title="Not available" data-view-component="true" class="Counter"></span> </a></li> <li data-view-component="true" class="d-inline-flex"> <a id="issues-tab" href="/benedekrozemberczki/awesome-gradient-boosting-papers/issues" data-tab-item="i1issues-tab" data-selected-links="repo_issues repo_labels repo_milestones /benedekrozemberczki/awesome-gradient-boosting-papers/issues" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-hotkey="g i" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Issues","target":"UNDERLINE_NAV.TAB"}" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navigation-item"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-issue-opened UnderlineNav-octicon d-none d-sm-inline"> <path d="M8 9.5a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path><path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Z"></path> </svg> <span data-content="Issues">Issues</span> <span id="issues-repo-tab-count" data-pjax-replace="" data-turbo-replace="" title="2" data-view-component="true" class="Counter">2</span> </a></li> <li data-view-component="true" class="d-inline-flex"> <a id="pull-requests-tab" href="/benedekrozemberczki/awesome-gradient-boosting-papers/pulls" data-tab-item="i2pull-requests-tab" data-selected-links="repo_pulls checks /benedekrozemberczki/awesome-gradient-boosting-papers/pulls" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-hotkey="g p" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Pull requests","target":"UNDERLINE_NAV.TAB"}" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navigation-item"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-git-pull-request UnderlineNav-octicon d-none d-sm-inline"> <path d="M1.5 3.25a2.25 2.25 0 1 1 3 2.122v5.256a2.251 2.251 0 1 1-1.5 0V5.372A2.25 2.25 0 0 1 1.5 3.25Zm5.677-.177L9.573.677A.25.25 0 0 1 10 .854V2.5h1A2.5 2.5 0 0 1 13.5 5v5.628a2.251 2.251 0 1 1-1.5 0V5a1 1 0 0 0-1-1h-1v1.646a.25.25 0 0 1-.427.177L7.177 3.427a.25.25 0 0 1 0-.354ZM3.75 2.5a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5Zm0 9.5a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5Zm8.25.75a.75.75 0 1 0 1.5 0 .75.75 0 0 0-1.5 0Z"></path> </svg> <span data-content="Pull requests">Pull requests</span> <span id="pull-requests-repo-tab-count" data-pjax-replace="" data-turbo-replace="" title="1" data-view-component="true" class="Counter">1</span> </a></li> <li data-view-component="true" class="d-inline-flex"> <a id="actions-tab" href="/benedekrozemberczki/awesome-gradient-boosting-papers/actions" data-tab-item="i3actions-tab" data-selected-links="repo_actions /benedekrozemberczki/awesome-gradient-boosting-papers/actions" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-hotkey="g a" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Actions","target":"UNDERLINE_NAV.TAB"}" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navigation-item"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-play UnderlineNav-octicon d-none d-sm-inline"> <path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Zm4.879-2.773 4.264 2.559a.25.25 0 0 1 0 .428l-4.264 2.559A.25.25 0 0 1 6 10.559V5.442a.25.25 0 0 1 .379-.215Z"></path> </svg> <span data-content="Actions">Actions</span> <span id="actions-repo-tab-count" data-pjax-replace="" data-turbo-replace="" title="Not available" data-view-component="true" class="Counter"></span> </a></li> <li data-view-component="true" class="d-inline-flex"> <a id="projects-tab" href="/benedekrozemberczki/awesome-gradient-boosting-papers/projects" data-tab-item="i4projects-tab" data-selected-links="repo_projects new_repo_project repo_project /benedekrozemberczki/awesome-gradient-boosting-papers/projects" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-hotkey="g b" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Projects","target":"UNDERLINE_NAV.TAB"}" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navigation-item"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-table UnderlineNav-octicon d-none d-sm-inline"> <path d="M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v12.5A1.75 1.75 0 0 1 14.25 16H1.75A1.75 1.75 0 0 1 0 14.25ZM6.5 6.5v8h7.75a.25.25 0 0 0 .25-.25V6.5Zm8-1.5V1.75a.25.25 0 0 0-.25-.25H6.5V5Zm-13 1.5v7.75c0 .138.112.25.25.25H5v-8ZM5 5V1.5H1.75a.25.25 0 0 0-.25.25V5Z"></path> </svg> <span data-content="Projects">Projects</span> <span id="projects-repo-tab-count" data-pjax-replace="" data-turbo-replace="" title="0" hidden="hidden" data-view-component="true" class="Counter">0</span> </a></li> <li data-view-component="true" class="d-inline-flex"> <a id="security-tab" href="/benedekrozemberczki/awesome-gradient-boosting-papers/security" data-tab-item="i5security-tab" data-selected-links="security overview alerts policy token_scanning code_scanning /benedekrozemberczki/awesome-gradient-boosting-papers/security" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-hotkey="g s" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Security","target":"UNDERLINE_NAV.TAB"}" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navigation-item"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-shield UnderlineNav-octicon d-none d-sm-inline"> <path d="M7.467.133a1.748 1.748 0 0 1 1.066 0l5.25 1.68A1.75 1.75 0 0 1 15 3.48V7c0 1.566-.32 3.182-1.303 4.682-.983 1.498-2.585 2.813-5.032 3.855a1.697 1.697 0 0 1-1.33 0c-2.447-1.042-4.049-2.357-5.032-3.855C1.32 10.182 1 8.566 1 7V3.48a1.75 1.75 0 0 1 1.217-1.667Zm.61 1.429a.25.25 0 0 0-.153 0l-5.25 1.68a.25.25 0 0 0-.174.238V7c0 1.358.275 2.666 1.057 3.86.784 1.194 2.121 2.34 4.366 3.297a.196.196 0 0 0 .154 0c2.245-.956 3.582-2.104 4.366-3.298C13.225 9.666 13.5 8.36 13.5 7V3.48a.251.251 0 0 0-.174-.237l-5.25-1.68ZM8.75 4.75v3a.75.75 0 0 1-1.5 0v-3a.75.75 0 0 1 1.5 0ZM9 10.5a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <span data-content="Security">Security</span> <include-fragment src="/benedekrozemberczki/awesome-gradient-boosting-papers/security/overall-count" accept="text/fragment+html"></include-fragment> </a></li> <li data-view-component="true" class="d-inline-flex"> <a id="insights-tab" href="/benedekrozemberczki/awesome-gradient-boosting-papers/pulse" data-tab-item="i6insights-tab" data-selected-links="repo_graphs repo_contributors dependency_graph dependabot_updates pulse people community /benedekrozemberczki/awesome-gradient-boosting-papers/pulse" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Insights","target":"UNDERLINE_NAV.TAB"}" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navigation-item"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-graph UnderlineNav-octicon d-none d-sm-inline"> <path d="M1.5 1.75V13.5h13.75a.75.75 0 0 1 0 1.5H.75a.75.75 0 0 1-.75-.75V1.75a.75.75 0 0 1 1.5 0Zm14.28 2.53-5.25 5.25a.75.75 0 0 1-1.06 0L7 7.06 4.28 9.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.25-3.25a.75.75 0 0 1 1.06 0L10 7.94l4.72-4.72a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042Z"></path> </svg> <span data-content="Insights">Insights</span> <span id="insights-repo-tab-count" data-pjax-replace="" data-turbo-replace="" title="Not available" data-view-component="true" class="Counter"></span> </a></li> </ul> <div style="visibility:hidden;" data-view-component="true" class="UnderlineNav-actions js-responsive-underlinenav-overflow position-absolute pr-3 pr-md-4 pr-lg-5 right-0"> <action-menu data-select-variant="none" data-view-component="true"> <focus-group direction="vertical" mnemonics retain> <button id="action-menu-40409453-17fe-4793-9a56-99727f45bcf6-button" popovertarget="action-menu-40409453-17fe-4793-9a56-99727f45bcf6-overlay" aria-controls="action-menu-40409453-17fe-4793-9a56-99727f45bcf6-list" aria-haspopup="true" aria-labelledby="tooltip-c9474656-9092-4eae-96a3-41a6df5c9a06" type="button" data-view-component="true" class="Button Button--iconOnly Button--secondary Button--medium UnderlineNav-item"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-kebab-horizontal Button-visual"> <path d="M8 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3ZM1.5 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Zm13 0a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path> </svg> </button><tool-tip id="tooltip-c9474656-9092-4eae-96a3-41a6df5c9a06" for="action-menu-40409453-17fe-4793-9a56-99727f45bcf6-button" popover="manual" data-direction="s" data-type="label" data-view-component="true" class="sr-only position-absolute">Additional navigation options</tool-tip> <anchored-position data-target="action-menu.overlay" id="action-menu-40409453-17fe-4793-9a56-99727f45bcf6-overlay" anchor="action-menu-40409453-17fe-4793-9a56-99727f45bcf6-button" align="start" side="outside-bottom" anchor-offset="normal" popover="auto" data-view-component="true"> <div data-view-component="true" class="Overlay Overlay--size-auto"> <div data-view-component="true" class="Overlay-body Overlay-body--paddingNone"> <action-list> <div data-view-component="true"> <ul aria-labelledby="action-menu-40409453-17fe-4793-9a56-99727f45bcf6-button" id="action-menu-40409453-17fe-4793-9a56-99727f45bcf6-list" role="menu" data-view-component="true" class="ActionListWrap--inset ActionListWrap"> <li hidden="hidden" data-menu-item="i0code-tab" data-targets="action-list.items" role="none" data-view-component="true" class="ActionListItem"> <a tabindex="-1" id="item-52b3e86d-4f6c-4ccb-a14c-c24058b5f20b" href="/benedekrozemberczki/awesome-gradient-boosting-papers" role="menuitem" data-view-component="true" class="ActionListContent ActionListContent--visual16"> <span class="ActionListItem-visual ActionListItem-visual--leading"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-code"> <path d="m11.28 3.22 4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734L13.94 8l-3.72-3.72a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215Zm-6.56 0a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042L2.06 8l3.72 3.72a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L.47 8.53a.75.75 0 0 1 0-1.06Z"></path> </svg> </span> <span data-view-component="true" class="ActionListItem-label"> Code </span> </a> </li> <li hidden="hidden" data-menu-item="i1issues-tab" data-targets="action-list.items" role="none" data-view-component="true" class="ActionListItem"> <a tabindex="-1" id="item-523be1f6-7212-468f-9038-3f093a4b90f9" href="/benedekrozemberczki/awesome-gradient-boosting-papers/issues" role="menuitem" data-view-component="true" class="ActionListContent ActionListContent--visual16"> <span class="ActionListItem-visual ActionListItem-visual--leading"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-issue-opened"> <path d="M8 9.5a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path><path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Z"></path> </svg> </span> <span data-view-component="true" class="ActionListItem-label"> Issues </span> </a> </li> <li hidden="hidden" data-menu-item="i2pull-requests-tab" data-targets="action-list.items" role="none" data-view-component="true" class="ActionListItem"> <a tabindex="-1" id="item-0fc2db96-2cc5-46e9-aa76-a92b71032f2d" href="/benedekrozemberczki/awesome-gradient-boosting-papers/pulls" role="menuitem" data-view-component="true" class="ActionListContent ActionListContent--visual16"> <span class="ActionListItem-visual ActionListItem-visual--leading"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-git-pull-request"> <path d="M1.5 3.25a2.25 2.25 0 1 1 3 2.122v5.256a2.251 2.251 0 1 1-1.5 0V5.372A2.25 2.25 0 0 1 1.5 3.25Zm5.677-.177L9.573.677A.25.25 0 0 1 10 .854V2.5h1A2.5 2.5 0 0 1 13.5 5v5.628a2.251 2.251 0 1 1-1.5 0V5a1 1 0 0 0-1-1h-1v1.646a.25.25 0 0 1-.427.177L7.177 3.427a.25.25 0 0 1 0-.354ZM3.75 2.5a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5Zm0 9.5a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5Zm8.25.75a.75.75 0 1 0 1.5 0 .75.75 0 0 0-1.5 0Z"></path> </svg> </span> <span data-view-component="true" class="ActionListItem-label"> Pull requests </span> </a> </li> <li hidden="hidden" data-menu-item="i3actions-tab" data-targets="action-list.items" role="none" data-view-component="true" class="ActionListItem"> <a tabindex="-1" id="item-ddcb8cde-8ec7-40ae-b65d-6e5e51b715a5" href="/benedekrozemberczki/awesome-gradient-boosting-papers/actions" role="menuitem" data-view-component="true" class="ActionListContent ActionListContent--visual16"> <span class="ActionListItem-visual ActionListItem-visual--leading"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-play"> <path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Zm4.879-2.773 4.264 2.559a.25.25 0 0 1 0 .428l-4.264 2.559A.25.25 0 0 1 6 10.559V5.442a.25.25 0 0 1 .379-.215Z"></path> </svg> </span> <span data-view-component="true" class="ActionListItem-label"> Actions </span> </a> </li> <li hidden="hidden" data-menu-item="i4projects-tab" data-targets="action-list.items" role="none" data-view-component="true" class="ActionListItem"> <a tabindex="-1" id="item-cc2ce10e-ca83-43d5-b6ac-89f030d7c7d0" href="/benedekrozemberczki/awesome-gradient-boosting-papers/projects" role="menuitem" data-view-component="true" class="ActionListContent ActionListContent--visual16"> <span class="ActionListItem-visual ActionListItem-visual--leading"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-table"> <path d="M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v12.5A1.75 1.75 0 0 1 14.25 16H1.75A1.75 1.75 0 0 1 0 14.25ZM6.5 6.5v8h7.75a.25.25 0 0 0 .25-.25V6.5Zm8-1.5V1.75a.25.25 0 0 0-.25-.25H6.5V5Zm-13 1.5v7.75c0 .138.112.25.25.25H5v-8ZM5 5V1.5H1.75a.25.25 0 0 0-.25.25V5Z"></path> </svg> </span> <span data-view-component="true" class="ActionListItem-label"> Projects </span> </a> </li> <li hidden="hidden" data-menu-item="i5security-tab" data-targets="action-list.items" role="none" data-view-component="true" class="ActionListItem"> <a tabindex="-1" id="item-aed61e8c-4a71-485e-bd06-a6f2154ec4b4" href="/benedekrozemberczki/awesome-gradient-boosting-papers/security" role="menuitem" data-view-component="true" class="ActionListContent ActionListContent--visual16"> <span class="ActionListItem-visual ActionListItem-visual--leading"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-shield"> <path d="M7.467.133a1.748 1.748 0 0 1 1.066 0l5.25 1.68A1.75 1.75 0 0 1 15 3.48V7c0 1.566-.32 3.182-1.303 4.682-.983 1.498-2.585 2.813-5.032 3.855a1.697 1.697 0 0 1-1.33 0c-2.447-1.042-4.049-2.357-5.032-3.855C1.32 10.182 1 8.566 1 7V3.48a1.75 1.75 0 0 1 1.217-1.667Zm.61 1.429a.25.25 0 0 0-.153 0l-5.25 1.68a.25.25 0 0 0-.174.238V7c0 1.358.275 2.666 1.057 3.86.784 1.194 2.121 2.34 4.366 3.297a.196.196 0 0 0 .154 0c2.245-.956 3.582-2.104 4.366-3.298C13.225 9.666 13.5 8.36 13.5 7V3.48a.251.251 0 0 0-.174-.237l-5.25-1.68ZM8.75 4.75v3a.75.75 0 0 1-1.5 0v-3a.75.75 0 0 1 1.5 0ZM9 10.5a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> </span> <span data-view-component="true" class="ActionListItem-label"> Security </span> </a> </li> <li hidden="hidden" data-menu-item="i6insights-tab" data-targets="action-list.items" role="none" data-view-component="true" class="ActionListItem"> <a tabindex="-1" id="item-1b261d10-c4f8-4c43-905a-e43331aef7b7" href="/benedekrozemberczki/awesome-gradient-boosting-papers/pulse" role="menuitem" data-view-component="true" class="ActionListContent ActionListContent--visual16"> <span class="ActionListItem-visual ActionListItem-visual--leading"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-graph"> <path d="M1.5 1.75V13.5h13.75a.75.75 0 0 1 0 1.5H.75a.75.75 0 0 1-.75-.75V1.75a.75.75 0 0 1 1.5 0Zm14.28 2.53-5.25 5.25a.75.75 0 0 1-1.06 0L7 7.06 4.28 9.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.25-3.25a.75.75 0 0 1 1.06 0L10 7.94l4.72-4.72a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042Z"></path> </svg> </span> <span data-view-component="true" class="ActionListItem-label"> Insights </span> </a> </li> </ul> </div></action-list> </div> </div></anchored-position> </focus-group> </action-menu></div> </nav> </div> <turbo-frame id="repo-content-turbo-frame" target="_top" data-turbo-action="advance" class=""> <div id="repo-content-pjax-container" class="repository-content " > <h1 class='sr-only'>benedekrozemberczki/awesome-gradient-boosting-papers</h1> <div class="clearfix container-xl px-md-4 px-lg-5 px-3"> <div> <div style="max-width: 100%" data-view-component="true" class="Layout Layout--flowRow-until-md react-repos-overview-margin Layout--sidebarPosition-end Layout--sidebarPosition-flowRow-end"> <div data-view-component="true" class="Layout-main"> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_dompurify_dist_purify_es_mjs-dd1d3ea6a436.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/vendors-node_modules_tanstack_query-core_build_modern_queryObserver_js-node_modules_tanstack_-defd52-843b41414e0e.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/ui_packages_aria-live_aria-live_ts-ui_packages_promise-with-resolvers-polyfill_promise-with-r-17c672-34345cb18aac.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/ui_packages_paths_index_ts-3adbcf6faa83.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/ui_packages_ref-selector_RefSelector_tsx-7496afc3784d.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/ui_packages_commit-attribution_index_ts-ui_packages_commit-checks-status_index_ts-ui_packages-7094d4-b869a469ca5e.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/ui_packages_hydro-analytics_hydro-analytics_ts-ui_packages_verified-fetch_verified-fetch_ts-u-4672d1-96a19eaeffb7.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/ui_packages_code-view-shared_hooks_use-canonical-object_ts-ui_packages_code-view-shared_hooks-d63960-3a5579c864b4.js"></script> <script crossorigin="anonymous" defer="defer" type="application/javascript" src="https://github.githubassets.com/assets/repos-overview-c97351639a70.js"></script> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/primer-react.248e2356ac373ce2e5c1.module.css" /> <link crossorigin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/repos-overview.0ee7cac3ab511a65d9f9.module.css" /> <react-partial partial-name="repos-overview" data-ssr="true" data-attempted-ssr="true" > <script type="application/json" data-target="react-partial.embeddedData">{"props":{"initialPayload":{"allShortcutsEnabled":false,"path":"/","repo":{"id":186163475,"defaultBranch":"master","name":"awesome-gradient-boosting-papers","ownerLogin":"benedekrozemberczki","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2019-05-11T17:39:03.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/17380887?v=4","public":true,"private":false,"isOrgOwned":false},"currentUser":null,"refInfo":{"name":"master","listCacheKey":"v0:1638013270.311727","canEdit":false,"refType":"branch","currentOid":"6d89978fc5c98c89d4c09fbbfd3caaf60ef9c2b0"},"tree":{"items":[{"name":".github","path":".github","contentType":"directory"},{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README.md","path":"README.md","contentType":"file"},{"name":"awesome.py","path":"awesome.py","contentType":"file"},{"name":"boosting.gif","path":"boosting.gif","contentType":"file"},{"name":"code-of-conduct.md","path":"code-of-conduct.md","contentType":"file"},{"name":"contributing.md","path":"contributing.md","contentType":"file"}],"templateDirectorySuggestionUrl":null,"readme":null,"totalCount":7,"showBranchInfobar":false},"fileTree":null,"fileTreeProcessingTime":null,"foldersToFetch":[],"treeExpanded":false,"symbolsExpanded":false,"isOverview":true,"overview":{"banners":{"shouldRecommendReadme":false,"isPersonalRepo":false,"showUseActionBanner":false,"actionSlug":null,"actionId":null,"showProtectBranchBanner":false,"publishBannersInfo":{"dismissActionNoticePath":"/settings/dismiss-notice/publish_action_from_repo","releasePath":"/benedekrozemberczki/awesome-gradient-boosting-papers/releases/new?marketplace=true","showPublishActionBanner":false},"interactionLimitBanner":null,"showInvitationBanner":false,"inviterName":null,"actionsMigrationBannerInfo":{"releaseTags":[],"showImmutableActionsMigrationBanner":false,"initialMigrationStatus":null}},"codeButton":{"contactPath":"/contact","isEnterprise":false,"local":{"protocolInfo":{"httpAvailable":true,"sshAvailable":null,"httpUrl":"https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers.git","showCloneWarning":null,"sshUrl":null,"sshCertificatesRequired":null,"sshCertificatesAvailable":null,"ghCliUrl":"gh repo clone benedekrozemberczki/awesome-gradient-boosting-papers","defaultProtocol":"http","newSshKeyUrl":"/settings/ssh/new","setProtocolPath":"/users/set_protocol"},"platformInfo":{"cloneUrl":"https://desktop.github.com","showVisualStudioCloneButton":false,"visualStudioCloneUrl":"https://windows.github.com","showXcodeCloneButton":false,"xcodeCloneUrl":"xcode://clone?repo=https%3A%2F%2Fgithub.com%2Fbenedekrozemberczki%2Fawesome-gradient-boosting-papers","zipballUrl":"/benedekrozemberczki/awesome-gradient-boosting-papers/archive/refs/heads/master.zip"}},"newCodespacePath":"/codespaces/new?hide_repo_select=true\u0026repo=186163475"},"popovers":{"rename":null,"renamedParentRepo":null},"commitCount":"206","overviewFiles":[{"displayName":"README.md","repoName":"awesome-gradient-boosting-papers","refName":"master","path":"README.md","preferredFileType":"readme","tabName":"README","richText":"\u003carticle class=\"markdown-body entry-content container-lg\" itemprop=\"text\"\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAwesome Gradient Boosting Research Papers.\u003c/h1\u003e\u003ca id=\"user-content-awesome-gradient-boosting-research-papers\" class=\"anchor\" aria-label=\"Permalink: Awesome Gradient Boosting Research Papers.\" href=\"#awesome-gradient-boosting-research-papers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/sindresorhus/awesome\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8693bde04030b1670d5097703441005eba34240c32d1df1eb82a5f0d6716518e/68747470733a2f2f63646e2e7261776769742e636f6d2f73696e647265736f726875732f617765736f6d652f643733303566333864323966656437386661383536353265336136336531353464643865383832392f6d656469612f62616467652e737667\" alt=\"Awesome\" data-canonical-src=\"https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://makeapullrequest.com\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/88482ebfc5e3e4f2d667148ab6a3eb55948789f1dba71dfa0eb2e05afe02958c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5052732d77656c636f6d652d627269676874677265656e2e7376673f7374796c653d666c61742d737175617265\" alt=\"PRs Welcome\" data-canonical-src=\"https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2982d26d63a27ecb81c1160e90d956bd83a6391e00002b3c6b2befb3a2214d48/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f62656e6564656b726f7a656d626572637a6b692f617765736f6d652d6772616469656e742d626f6f7374696e672d7061706572732e7376673f636f6c6f723d626c7565\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2982d26d63a27ecb81c1160e90d956bd83a6391e00002b3c6b2befb3a2214d48/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f62656e6564656b726f7a656d626572637a6b692f617765736f6d652d6772616469656e742d626f6f7374696e672d7061706572732e7376673f636f6c6f723d626c7565\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/benedekrozemberczki/awesome-gradient-boosting-papers.svg?color=blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers/archive/master.zip\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8de93fc248cea4512d237cd88326b05ef11b3f22dae091ac8a180f68f23dcb0a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f7265706f2d73697a652f62656e6564656b726f7a656d626572637a6b692f617765736f6d652d6772616469656e742d626f6f7374696e672d7061706572732e737667\" alt=\"repo size\" data-canonical-src=\"https://img.shields.io/github/repo-size/benedekrozemberczki/awesome-gradient-boosting-papers.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://twitter.com/intent/follow?screen_name=benrozemberczki\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dcb0f569a415bbaea3fcda28bb9fb3f9159dea5706937e0046a7853072228c89/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f62656e726f7a656d626572637a6b693f7374796c653d736f6369616c266c6f676f3d74776974746572\" alt=\"benedekrozemberczki\" data-canonical-src=\"https://img.shields.io/twitter/follow/benrozemberczki?style=social\u0026amp;logo=twitter\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/boosting.gif\"\u003e\u003cimg width=\"450\" src=\"/benedekrozemberczki/awesome-gradient-boosting-papers/raw/master/boosting.gif\" data-animated-image=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003chr\u003e\n\u003cp dir=\"auto\"\u003eA curated list of gradient and adaptive boosting papers with implementations from the following conferences:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMachine learning\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://nips.cc/\" rel=\"nofollow\"\u003eNeurIPS\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://icml.cc/\" rel=\"nofollow\"\u003eICML\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://iclr.cc/\" rel=\"nofollow\"\u003eICLR\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eComputer vision\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"http://cvpr2019.thecvf.com/\" rel=\"nofollow\"\u003eCVPR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://iccv2019.thecvf.com/\" rel=\"nofollow\"\u003eICCV\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://eccv2018.org/\" rel=\"nofollow\"\u003eECCV\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eNatural language processing\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"http://www.acl2019.org/EN/index.xhtml\" rel=\"nofollow\"\u003eACL\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://naacl2019.org/\" rel=\"nofollow\"\u003eNAACL\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.emnlp-ijcnlp2019.org/\" rel=\"nofollow\"\u003eEMNLP\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eData\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://www.kdd.org/\" rel=\"nofollow\"\u003eKDD\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cikmconference.org/\" rel=\"nofollow\"\u003eCIKM\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://icdm2019.bigke.org/\" rel=\"nofollow\"\u003eICDM\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.siam.org/Conferences/CM/Conference/sdm19\" rel=\"nofollow\"\u003eSDM\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://pakdd2019.medmeeting.org\" rel=\"nofollow\"\u003ePAKDD\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://ecmlpkdd2019.org\" rel=\"nofollow\"\u003ePKDD/ECML\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://recsys.acm.org/\" rel=\"nofollow\"\u003eRECSYS\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sigir.org/\" rel=\"nofollow\"\u003eSIGIR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www2019.thewebconf.org/\" rel=\"nofollow\"\u003eWWW\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/www.wsdm-conference.org\"\u003eWSDM\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eArtificial intelligence\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://www.aaai.org/\" rel=\"nofollow\"\u003eAAAI\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aistats.org/\" rel=\"nofollow\"\u003eAISTATS\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://e-nns.org/icann2019/\" rel=\"nofollow\"\u003eICANN\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/\" rel=\"nofollow\"\u003eIJCAI\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.auai.org/\" rel=\"nofollow\"\u003eUAI\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eSimilar collections about \u003ca href=\"https://github.com/benedekrozemberczki/awesome-graph-classification\"\u003egraph classification\u003c/a\u003e, \u003ca href=\"https://github.com/benedekrozemberczki/awesome-decision-tree-papers\"\u003eclassification/regression tree\u003c/a\u003e, \u003ca href=\"https://github.com/benedekrozemberczki/awesome-fraud-detection-papers\"\u003efraud detection\u003c/a\u003e, \u003ca href=\"https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers\"\u003eMonte Carlo tree search\u003c/a\u003e, and \u003ca href=\"https://github.com/benedekrozemberczki/awesome-community-detection\"\u003ecommunity detection\u003c/a\u003e papers with implementations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2023\u003c/h2\u003e\u003ca id=\"user-content-2023\" class=\"anchor\" aria-label=\"Permalink: 2023\" href=\"#2023\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eComputing Abductive Explanations for Boosted Trees (AISTATS 2023)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2209.07740\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Off-Policy Learning (AISTATS 2023)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBen London, Levi Lu, Ted Sandler, Thorsten Joachims\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2208.01148\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eVariational Boosted Soft Trees (AISTATS 2023)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2302.10706\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKrylov-Bellman boosting: Super-linear policy evaluation in general state spaces (AISTATS 2023)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eEric Xia, Martin J. Wainwright\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2210.11377\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFairGBM: Gradient Boosting with Fairness Constraints (ICLR 2023)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAndré Ferreira Cruz, Catarina Belém, João Bravo, Pedro Saleiro, Pedro Bizarro\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2209.07850\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGradient Boosting Performs Gaussian Process Inference (ICLR 2023)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2206.05608\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2022\u003c/h2\u003e\u003ca id=\"user-content-2022\" class=\"anchor\" aria-label=\"Permalink: 2022\" href=\"#2022\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion (AAAI 2022)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYiheng Sun, Tian Lu, Cong Wang, Yuan Li, Huaiyu Fu, Jingran Dong, Yunjie Xu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2112.02365\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Resilient Distributed Boosting Algorithm (ICML 2022)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYuval Filmus, Idan Mehalel, Shay Moran\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2206.04713\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFast Provably Robust Decision Trees and Boosting (ICML 2022)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://proceedings.mlr.press/v162/guo22h.html\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBuilding Robust Ensembles via Margin Boosting (ICML 2022)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2206.03362\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRetrieval-Based Gradient Boosting Decision Trees for Disease Risk Assessment (KDD 2022)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHandong Ma, Jiahang Cao, Yuchen Fang, Weinan Zhang, Wenbo Sheng, Shaodian Zhang, Yong Yu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/doi/abs/10.1145/3534678.3539052\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFederated Functional Gradient Boosting (AISTATS 2022)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2103.06972\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics (AISTATS 2022)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDaniel Csillag, Carolina Piazza, Thiago Ramos, João Vitor Romano, Roberto I. Oliveira, Paulo Orenstein\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://proceedings.mlr.press/v151/csillag22a.html\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2021\u003c/h2\u003e\u003ca id=\"user-content-2021\" class=\"anchor\" aria-label=\"Permalink: 2021\" href=\"#2021\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePrecision-based Boosting (AAAI 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMohammad Hossein Nikravan, Marjan Movahedan, Sandra Zilles\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ojs.aaai.org/index.php/AAAI/article/view/17105\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBNN: Boosting Neural Network Framework Utilizing Limited Amount of Data (CIKM 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAmit Livne, Roy Dor, Bracha Shapira, Lior Rokach\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/doi/abs/10.1145/3459637.3482414\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUnsupervised Domain Adaptation for Static Malware Detection based on Gradient Boosting Trees (CIKM 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePanpan Qi, Wei Wang, Lei Zhu, See-Kiong Ng\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/doi/pdf/10.1145/3459637.3482400\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eIndividually Fair Gradient Boosting (ICLR 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2103.16785\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAre Neural Rankers still Outperformed by Gradient Boosted Decision Trees (ICLR 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://iclr.cc/virtual/2021/spotlight/3536\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdaGCN: Adaboosting Graph Convolutional Networks into Deep Models (ICLR 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKe Sun, Zhanxing Zhu, Zhouchen Lin\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1908.05081\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/datake/AdaGCN\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUncertainty in Gradient Boosting via Ensembles (ICLR 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAndrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2006.10562\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoost then Convolve: Gradient Boosting Meets Graph Neural Networks (ICLR 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSergei Ivanov, Liudmila Prokhorenkova\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2101.08543\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGBHT: Gradient Boosting Histogram Transform for Density Estimation (ICML 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2106.05738\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting for Online Convex Optimization (ICML 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eElad Hazan, Karan Singh\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2102.09305\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAccuracy, Interpretability, and Differential Privacy via Explainable Boosting (ICML 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHarsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2106.09680\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSGLB: Stochastic Gradient Langevin Boosting (ICML 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAleksei Ustimenko, Liudmila Prokhorenkova\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2001.07248\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSelf-boosting for Feature Distillation (IJCAI 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYulong Pei, Yanyun Qu, Junping Zhang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/proceedings/2021/131\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Variational Inference With Locally Adaptive Step-Sizes (IJCAI 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2105.09240\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eProbabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression (KDD 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eOlivier Sprangers, Sebastian Schelter, Maarten de Rijke\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2106.01682\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTask-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction (KDD 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMingcheng Chen, Zhenghui Wang, Zhiyun Zhao, Weinan Zhang, Xiawei Guo, Jian Shen, Yanru Qu, Jieli Lu, Min Xu, Yu Xu, Tiange Wang, Mian Li, Weiwei Tu, Yong Yu, Yufang Bi, Weiqing Wang, Guang Ning\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2108.07107\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBetter Short than Greedy: Interpretable Models through Optimal Rule Boosting (SDM 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMario Boley, Simon Teshuva, Pierre Le Bodic, Geoffrey I. Webb\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2101.08380\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2020\u003c/h2\u003e\u003ca id=\"user-content-2020\" class=\"anchor\" aria-label=\"Permalink: 2020\" href=\"#2020\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains (AAAI 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHarsha Kokel, Phillip Odom, Shuo Yang, Sriraam Natarajan\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://personal.utdallas.edu/~sriraam.natarajan/Papers/Kokel_AAAI20.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/harshakokel/KiGB\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePractical Federated Gradient Boosting Decision Trees (AAAI 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eQinbin Li, Zeyi Wen, Bingsheng He\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1911.04206\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePrivacy-Preserving Gradient Boosting Decision Trees (AAAI 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eQinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1911.04209\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAccelerating Gradient Boosting Machines (AISTATS 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHaihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1903.08708\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eScalable Feature Selection for Multitask Gradient Boosted Trees (AISTATS 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCuize Han, Nikhil Rao, Daria Sorokina, Karthik Subbian\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v108/han20a.html\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFunctional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees (AISTATS 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAtsushi Nitanda, Taiji Suzuki\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v108/nitanda20a.html\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLearning Optimal Decision Trees with MaxSAT and its Integration in AdaBoost (IJCAI 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHao Hu, Mohamed Siala, Emmanuel Hebrard, Marie-José Huguet\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/Proceedings/2020/163\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMixBoost: Synthetic Oversampling using Boosted Mixup for Handling Extreme Imbalance (ICDM 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAnubha Kabra, Ayush Chopra, Nikaash Puri, Pinkesh Badjatiya, Sukriti Verma, Piyush Gupta, Balaji Krishnamurthy\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2009.01571\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting for Control of Dynamical Systems (ICML 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNaman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1906.08720\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eQuantum Boosting (ICML 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSrinivasan Arunachalam, Reevu Maity\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2002.05056\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Histogram Transform for Regression (ICML 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYuchao Cai, Hanyuan Hang, Hanfang Yang, Zhouchen Lin\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://proceedings.icml.cc/static/paper_files/icml/2020/2360-Paper.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Frank-Wolfe by Chasing Gradients (ICML 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCyrille W. Combettes, Sebastian Pokutta\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2003.06369\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNGBoost: Natural Gradient Boosting for Probabilistic Prediction (ICML 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTony Duan, Avati Anand, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1910.03225\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/stanfordmlgroup/ngboost\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOnline Agnostic Boosting via Regret Minimization (NeurIPS 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2003.01150\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst Case Rates (NeurIPS 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKaiwen Zhou, Anthony Man-Cho So, James Cheng\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2005.12061\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOptimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks (NeurIPS 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKenta Oono, Taiji Suzuki\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2006.08550\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/delta2323/GB-GNN\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGradient Boosted Normalizing Flows (NeurIPS 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRobert Giaquinto, Arindam Banerjee\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2002.11896\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/robert-giaquinto/gradient-boosted-normalizing-flows\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eHyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems (WSDM 2020)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, Xiaoli Li\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1809.01703\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2019\u003c/h2\u003e\u003ca id=\"user-content-2019\" class=\"anchor\" aria-label=\"Permalink: 2019\" href=\"#2019\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eInduction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME (AAAI 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFarhad Shakerin, Gopal Gupta\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1808.00629\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eVerifying Robustness of Gradient Boosted Models (AAAI 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGil Einziger, Maayan Goldstein, Yaniv Sa'ar, Itai Segall\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/pdf/1906.10991.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOnline Multiclass Boosting with Bandit Feedback (AISTATS 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDaniel T. Zhang, Young Hun Jung, Ambuj Tewari\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1810.05290\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdaFair: Cumulative Fairness Adaptive Boosting (CIKM 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eVasileios Iosifidis, Eirini Ntoutsi\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1909.08982\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eInterpretable MTL from Heterogeneous Domains using Boosted Tree (CIKM 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYa-Lin Zhang, Longfei Li\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=3357384.3358072\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdversarial Training of Gradient-Boosted Decision Trees (CIKM 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eStefano Calzavara, Claudio Lucchese, Gabriele Tolomei\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.dais.unive.it/~calzavara/papers/cikm19.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFair Adversarial Gradient Tree Boosting (ICDM 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eVincent Grari, Boris Ruf, Sylvain Lamprier, Marcin Detyniecki\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1911.05369\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Density Estimation Remastered (ICML 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZac Cranko, Richard Nock\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1803.08178\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLossless or Quantized Boosting with Integer Arithmetic (ICML 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRichard Nock, Robert C. Williamson\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v97/nock19a.html\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOptimal Minimal Margin Maximization with Boosting (ICML 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlexander Mathiasen, Kasper Green Larsen, Allan Grønlund\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1901.10789\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKatalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number (ICML 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1809.06754\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting for Comparison-Based Learning (IJCAI 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMichaël Perrot, Ulrike von Luxburg\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1810.13333\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAugBoost: Gradient Boosting Enhanced with Step-Wise Feature Augmentation (IJCAI 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePhilip Tannor, Lior Rokach\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/proceedings/2019/0493.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGradient Boosting with Piece-Wise Linear Regression Trees (IJCAI 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYu Shi, Jian Li, Zhize Li\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1802.05640\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/GBDT-PL/GBDT-PL\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSpiderBoost and Momentum: Faster Variance Reduction Algorithms (NeurIPS 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://papers.nips.cc/paper/8511-spiderboost-and-momentum-faster-variance-reduction-algorithms\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFaster Boosting with Smaller Memory (NeurIPS 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJulaiti Alafate, Yoav Freund\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1901.09047\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRegularized Gradient Boosting (NeurIPS 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCorinna Cortes, Mehryar Mohri, Dmitry Storcheus\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/8784-regularized-gradient-boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMargin-Based Generalization Lower Bounds for Boosted Classifiers (NeurIPS 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAllan Grønlund, Lior Kamma, Kasper Green Larsen, Alexander Mathiasen, Jelani Nelson\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1909.12518\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMinimal Variance Sampling in Stochastic Gradient Boosting (NeurIPS 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBulat Ibragimov, Gleb Gusev\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/9645-minimal-variance-sampling-in-stochastic-gradient-boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUniversal Boosting Variational Inference (NeurIPS 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTrevor Campbell, Xinglong Li\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1906.01235\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eProvably Robust Boosted Decision Stumps and Trees against Adversarial Attacks (NeurIPS 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMaksym Andriushchenko, Matthias Hein\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1906.03526\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/max-andr/provably-robust-boosting\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBlock-distributed Gradient Boosted Trees (SIGIR 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTheodore Vasiloudis, Hyunsu Cho, Henrik Boström\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1904.10522\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLearning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning (SIGIR 2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eClaudio Lucchese, Franco Maria Nardini, Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang, Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/334579610_Learning_to_Rank_in_Theory_and_Practice_From_Gradient_Boosting_to_Neural_Networks_and_Unbiased_Learning\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2018\u003c/h2\u003e\u003ca id=\"user-content-2018\" class=\"anchor\" aria-label=\"Permalink: 2018\" href=\"#2018\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Generative Models (AAAI 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAditya Grover, Stefano Ermon\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/pdf/1702.08484.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ermongroup/bgm\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Variational Inference: an Optimization Perspective (AISTATS 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFrancesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1708.01733\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ratschlab/boosting-bbvi\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOnline Boosting Algorithms for Multi-label Ranking (AISTATS 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYoung Hun Jung, Ambuj Tewari\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1710.08079\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/yhjung88/OnlineMLRBoostingWithVFDT\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDualBoost: Handling Missing Values with Feature Weights and Weak Classifiers that Abstain (CIKM 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWeihong Wang, Jie Xu, Yang Wang, Chen Cai, Fang Chen\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://delivery.acm.org/10.1145/3270000/3269319/p1543-wang.pdf?ip=129.215.164.203\u0026amp;id=3269319\u0026amp;acc=ACTIVE%20SERVICE\u0026amp;key=C2D842D97AC95F7A%2EEB9E991028F4E1F1%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35\u0026amp;__acm__=1558633895_f01b39fd47b943fd01eade763a397e04\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFunctional Gradient Boosting based on Residual Network Perception (ICML 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAtsushi Nitanda, Taiji Suzuki\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1802.09031\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/anitan0925/ResFGB\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFinding Influential Training Samples for Gradient Boosted Decision Trees (ICML 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBoris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, Maarten de Rijke\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1802.06640\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLearning Deep ResNet Blocks Sequentially using Boosting Theory (ICML 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFurong Huang, Jordan T. Ash, John Langford, Robert E. Schapire\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1706.04964\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/JordanAsh/boostresnet\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUCBoost: A Boosting Approach to Tame Complexity and Optimality for Stochastic Bandits (IJCAI 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFang Liu, Sinong Wang, Swapna Buccapatnam, Ness B. Shroff\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/proceedings/2018/0338.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://smpybandits.github.io/docs/Policies.UCBoost.html\" rel=\"nofollow\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdaboost with Auto-Evaluation for Conversational Models (IJCAI 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJuncen Li, Ping Luo, Ganbin Zhou, Fen Lin, Cheng Niu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/proceedings/2018/0580.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEnsemble Neural Relation Extraction with Adaptive Boosting (IJCAI 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDongdong Yang, Senzhang Wang, Zhoujun Li\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/proceedings/2018/0630.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCatBoost: Unbiased Boosting with Categorical Features (NIPS 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLiudmila Ostroumova Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/7898-catboost-unbiased-boosting-with-categorical-features.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/catboost/catboost\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMultitask Boosting for Survival Analysis with Competing Risks (NIPS 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlexis Bellot, Mihaela van der Schaar\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/7413-multitask-boosting-for-survival-analysis-with-competing-risks\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMulti-Layered Gradient Boosting Decision Trees (NIPS 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJi Feng, Yang Yu, Zhi-Hua Zhou\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/7614-multi-layered-gradient-boosting-decision-trees.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kingfengji/mGBDT\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Sparse and Low-Rank Tensor Regression (NIPS 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1811.01158\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LifangHe/NeurIPS18_SURF\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSelective Gradient Boosting for Effective Learning to Rank (SIGIR 2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eClaudio Lucchese, Franco Maria Nardini, Raffaele Perego, Salvatore Orlando, Salvatore Trani\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://quickrank.isti.cnr.it/selective-data/selective-SIGIR2018.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hpclab/quickrank/blob/master/documentation/selective.md\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2017\u003c/h2\u003e\u003ca id=\"user-content-2017\" class=\"anchor\" aria-label=\"Permalink: 2017\" href=\"#2017\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting for Real-Time Multivariate Time Series Classification (AAAI 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHaishuai Wang, Jun Wu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14852/14241\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCross-Domain Sentiment Classification via Topic-Related TrAdaBoost (AAAI 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eXingchang Huang, Yanghui Rao, Haoran Xie, Tak-Lam Wong, Fu Lee Wang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/826c/c83d98a5c4c7dcc02be1f4dd9c27e2b99670.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/xchhuang/cross-domain-sentiment-classification\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eExtreme Gradient Boosting and Behavioral Biometrics (AAAI 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBenjamin Manning\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/8c6e/6c887d6d47dda3f0c73297fd4da516fef1ee.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFeaBoost: Joint Feature and Label Refinement for Semantic Segmentation (AAAI 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYulei Niu, Zhiwu Lu, Songfang Huang, Xin Gao, Ji-Rong Wen\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/d566/73be998b3ed38ccbb53551e38758ae8cfc9d.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Complementary Hash Tables for Fast Nearest Neighbor Search (AAAI 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eXianglong Liu, Cheng Deng, Yadong Mu, Zhujin Li\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14336\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGradient Boosting on Stochastic Data Streams (AISTATS 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1703.00377\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoostVHT: Boosting Distributed Streaming Decision Trees (CIKM 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTheodore Vasiloudis, Foteini Beligianni, Gianmarco De Francisci Morales\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://melmeric.files.wordpress.com/2010/05/boostvht-boosting-distributed-streaming-decision-trees.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features (CVPR 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eArthur Daniel Costea, Robert Varga, Sergiu Nedevschi\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://openaccess.thecvf.com/content_cvpr_2017/papers/Costea_Fast_Boosting_Based_CVPR_2017_paper.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBIER - Boosting Independent Embeddings Robustly (ICCV 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMichael Opitz, Georg Waltner, Horst Possegger, Horst Bischof\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://openaccess.thecvf.com/content_ICCV_2017/papers/Opitz_BIER_-_Boosting_ICCV_2017_paper.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mop/bier\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAn Analysis of Boosted Linear Classifiers on Noisy Data with Applications to Multiple-Instance Learning (ICDM 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRui Liu, Soumya Ray\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/8215501\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eVariational Boosting: Iteratively Refining Posterior Approximations (ICML 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAndrew C. Miller, Nicholas J. Foti, Ryan P. Adams\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1611.06585\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/andymiller/vboost\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Fitted Q-Iteration (ICML 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSamuele Tosatto, Matteo Pirotta, Carlo D'Eramo, Marcello Restelli\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v70/tosatto17a.html\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency (ICML 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRon Appel, Pietro Perona\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v70/appel17a.html\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/GuillaumeCollin/A-Simple-Multi-Class-Boosting-Framework-with-Theoretical-Guarantees-and-Empirical-Proficiency\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGradient Boosted Decision Trees for High Dimensional Sparse Output (ICML 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSi Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v70/si17a.html\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/springdaisy/GBDT\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLocal Topic Discovery via Boosted Ensemble of Nonnegative Matrix Factorization (IJCAI 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSangho Suh, Jaegul Choo, Joonseok Lee, Chandan K. Reddy\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://dmkd.cs.vt.edu/papers/IJCAI17.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/benedekrozemberczki/BoostedFactorization\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Zero-Shot Learning with Semantic Correlation Regularization (IJCAI 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTe Pi, Xi Li, Zhongfei (Mark) Zhang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1707.08008\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency (KDD 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYin Lou, Mikhail Obukhov\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://yinlou.github.io/papers/lou-kdd17.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCatBoost: Gradient Boosting with Categorical Features Support (NIPS 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAnna Veronika Dorogush, Vasily Ershov, Andrey Gulin\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1810.11363\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://catboost.ai/\" rel=\"nofollow\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCost Efficient Gradient Boosting (NIPS 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/6753-cost-efficient-gradient-boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/svenpeter42/LightGBM-CEGB\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdaGAN: Boosting Generative Models (NIPS 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIlya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1701.02386\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tolstikhin/adagan\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLightGBM: A Highly Efficient Gradient Boosting Decision Tree (NIPS 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGuolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://lightgbm.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEarly Stopping for Kernel Boosting Algorithms: A General Analysis with Localized Complexities (NIPS 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYuting Wei, Fanny Yang, Martin J. Wainwright\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1707.01543\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/fanny-yang/EarlyStoppingRKHS\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOnline Multiclass Boosting (NIPS 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYoung Hun Jung, Jack Goetz, Ambuj Tewari\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/6693-online-multiclass-boosting.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eStacking Bagged and Boosted Forests for Effective Automated Classification (SIGIR 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRaphael R. Campos, Sérgio D. Canuto, Thiago Salles, Clebson C. A. de Sá, Marcos André Gonçalves\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://homepages.dcc.ufmg.br/~rcampos/papers/sigir2017/appendix.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/raphaelcampos/stacking-bagged-boosted-forests\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees (WWW 2017)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eQian Zhao, Yue Shi, Liangjie Hong\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1311.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/grouplens/samantha\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2016\u003c/h2\u003e\u003ca id=\"user-content-2016\" class=\"anchor\" aria-label=\"Permalink: 2016\" href=\"#2016\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGroup Cost-Sensitive Boosting for Multi-Resolution Pedestrian Detection (AAAI 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChao Zhu, Yuxin Peng\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewFile/11898/12146\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nnikolaou/Cost-sensitive-Boosting-Tutorial\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCommunication Efficient Distributed Agnostic Boosting (AISTATS 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eShang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1506.06318\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLogistic Boosting Regression for Label Distribution Learning (CVPR 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChao Xing, Xin Geng, Hui Xue\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://zpascal.net/cvpr2016/Xing_Logistic_Boosting_Regression_CVPR_2016_paper.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eStructured Regression Gradient Boosting (CVPR 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFerran Diego, Fred A. Hamprecht\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://hci.iwr.uni-heidelberg.de/sites/default/files/publications/files/1037872734/diego_16_structured.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eL-EnsNMF: Boosted Local Topic Discovery via Ensemble of Nonnegative Matrix Factorization (ICDM 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSangho Suh, Jaegul Choo, Joonseok Lee, Chandan K. Reddy\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/7837872\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/benedekrozemberczki/BoostedFactorization\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMeta-Gradient Boosted Decision Tree Model for Weight and Target Learning (ICML 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v48/ustinovskiy16.html\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGeneralized Dictionary for Multitask Learning with Boosting (IJCAI 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBoyu Wang, Joelle Pineau\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/Proceedings/16/Papers/299.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSelf-Paced Boost Learning for Classification (IJCAI 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTe Pi, Xi Li, Zhongfei Zhang, Deyu Meng, Fei Wu, Jun Xiao, Yueting Zhuang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/31b6/ab4a0771d5b7405cacdd12c398b1c832729d.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eInteractive Martingale Boosting (IJCAI 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAshish Kulkarni, Pushpak Burange, Ganesh Ramakrishnan\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/Proceedings/16/Papers/124.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOptimal and Adaptive Algorithms for Online Boosting (IJCAI 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlina Beygelzimer, Satyen Kale, Haipeng Luo\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/Proceedings/16/Papers/614.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/blob/master/vowpalwabbit/boosting.cc\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews (IJCAI 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYunzhi Tan, Min Zhang, Yiqun Liu, Shaoping Ma\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/db63/89e0ca49ec0e4686e40604e7489cb4c0729d.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eXGBoost: A Scalable Tree Boosting System (KDD 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTianqi Chen, Carlos Guestrin\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dmlc/xgboost\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments (KDD 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlexey Poyarkov, Alexey Drutsa, Andrey Khalyavin, Gleb Gusev, Pavel Serdyukov\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.kdd.org/kdd2016/papers/files/adf0653-poyarkovA.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting with Abstention (NIPS 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCorinna Cortes, Giulia DeSalvo, Mehryar Mohri\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/6336-boosting-with-abstention\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques (NIPS 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eElad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/6109-seboost-boosting-stochastic-learning-using-subspace-optimization-techniques.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/eladrich/seboost\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eIncremental Boosting Convolutional Neural Network for Facial Action Unit Recognition (NIPS 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eShizhong Han, Zibo Meng, Ahmed-Shehab Khan, Yan Tong\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1707.05395\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sjsingh91/IB-CNN\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGeneralized BROOF-L2R: A General Framework for Learning to Rank Based on Boosting and Random Forests (SIGIR 2016)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eClebson C. A. de Sá, Marcos André Gonçalves, Daniel Xavier de Sousa, Thiago Salles\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=2911540\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2015\u003c/h2\u003e\u003ca id=\"user-content-2015\" class=\"anchor\" aria-label=\"Permalink: 2015\" href=\"#2015\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOnline Boosting Algorithms for Anytime Transfer and Multitask Learning (AAAI 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBoyu Wang, Joelle Pineau\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cs.mcgill.ca/~jpineau/files/bwang-aaai15.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Boosted Multi-Task Model for Pedestrian Detection with Occlusion Handling (AAAI 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChao Zhu, Yuxin Peng\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9879/9825\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEfficient Second-Order Gradient Boosting for Conditional Random Fields (AISTATS 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTianqi Chen, Sameer Singh, Ben Taskar, Carlos Guestrin\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v38/chen15b.html\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTumblr Blog Recommendation with Boosted Inductive Matrix Completion (CIKM 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDonghyuk Shin, Suleyman Cetintas, Kuang-Chih Lee, Inderjit S. Dhillon\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=2806578\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBasis mapping based boosting for object detection (CVPR 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHaoyu Ren, Ze-Nian Li\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/7298766\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTracking-by-Segmentation with Online Gradient Boosting Decision Tree (ICCV 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJeany Son, Ilchae Jung, Kayoung Park, Bohyung Han\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Son_Tracking-by-Segmentation_With_Online_ICCV_2015_paper.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://cvlab.postech.ac.kr/research/ogbdt_track/\" rel=\"nofollow\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLearning to Boost Filamentary Structure Segmentation (ICCV 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLin Gu, Li Cheng\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://isg.nist.gov/BII_2015/webPages/pages/2015_BII_program/PDFs/Day_3/Session_9/Abstract_Gu_Lin.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOptimal and Adaptive Algorithms for Online Boosting (ICML 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlina Beygelzimer, Satyen Kale, Haipeng Luo\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v37/beygelzimer15.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/blob/master/vowpalwabbit/boosting.cc\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRademacher Observations, Private Data, and Boosting (ICML 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRichard Nock, Giorgio Patrini, Arik Friedman\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1502.02322\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions (ICML 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTaehoon Lee, Sungroh Yoon\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/d0ad/beef3053e98dd88ff74f42744417bc65a729.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Direct Boosting Approach for Semi-supervised Classification (IJCAI 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eShaodan Zhai, Tian Xia, Zhongliang Li, Shaojun Wang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/Proceedings/15/Papers/565.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Boosting Algorithm for Item Recommendation with Implicit Feedback (IJCAI 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYong Liu, Peilin Zhao, Aixin Sun, Chunyan Miao\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/Proceedings/15/Papers/255.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/microsoft/recommenders\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTraining-Time Optimization of a Budgeted Booster (IJCAI 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYi Huang, Brian Powers, Lev Reyzin\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/Proceedings/15/Papers/504.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOptimal Action Extraction for Random Forests and Boosted Trees (KDD 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZhicheng Cui, Wenlin Chen, Yujie He, Yixin Chen\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cse.wustl.edu/~ychen/public/OAE.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOnline Gradient Boosting (NIPS 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1506.04820\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/crm416/online_boosting\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBROOF: Exploiting Out-of-Bag Errors Boosting and Random Forests for Effective Automated Classification (SIGIR 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThiago Salles, Marcos André Gonçalves, Victor Rodrigues, Leonardo C. da Rocha\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://homepages.dcc.ufmg.br/~tsalles/broof/appendix.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Search with Deep Understanding of Contents and Users (WSDM 2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKaihua Zhu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/282482189_Boosting_Search_with_Deep_Understanding_of_Contents_and_Users\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2014\u003c/h2\u003e\u003ca id=\"user-content-2014\" class=\"anchor\" aria-label=\"Permalink: 2014\" href=\"#2014\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn Boosting Sparse Parities (AAAI 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLev Reyzin\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8587\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eJoint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis (CVPR 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Shi_Joint_Coupled-Feature_Representation_2014_CVPR_paper.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFrom Categories to Individuals in Real Time - A Unified Boosting Approach (CVPR 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDavid Hall, Pietro Perona\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/6909424\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.vision.caltech.edu/~dhall/projects/CategoriesToIndividuals/\" rel=\"nofollow\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEfficient Boosted Exemplar-Based Face Detection (CVPR 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHaoxiang Li, Zhe Lin, Jonathan Brandt, Xiaohui Shen, Gang Hua\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://users.eecs.northwestern.edu/~xsh835/assets/cvpr14_exemplarfacedetection.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFacial Expression Recognition via a Boosted Deep Belief Network (CVPR 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePing Liu, Shizhong Han, Zibo Meng, Yan Tong\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/abstract/document/6909629\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eConfidence-Rated Multiple Instance Boosting for Object Detection (CVPR 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKarim Ali, Kate Saenko\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/6909708\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eThe Return of AdaBoost.MH: Multi-Class Hamming Trees (ICLR 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBalázs Kégl\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/pdf/1312.6086.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/aciditeam/acidano/blob/master/acidano/utils/cost.py\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDeep Boosting (ICML 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCorinna Cortes, Mehryar Mohri, Umar Syed\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v32/cortesb14.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/google/deepboost\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Convergence Rate Analysis for LogitBoost, MART and Their Variant (ICML 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePeng Sun, Tong Zhang, Jie Zhou\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v32/sunc14.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting with Online Binary Learners for the Multiclass Bandit Problem (ICML 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eShang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cc.gatech.edu/~schen351/paper/icml14boost.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Multi-Step Autoregressive Forecasts (ICML 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSouhaib Ben Taieb, Rob J. Hyndman\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v32/taieb14.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDynamic Programming Boosting for Discriminative Macro-Action Discovery (ICML 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLeonidas Lefakis, François Fleuret\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v32/lefakis14.html\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGuess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting (ICML 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eOscar Beijbom, Mohammad J. Saberian, David J. Kriegman, Nuno Vasconcelos\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v32/beijbom14.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Multi-Class Boosting Method with Direct Optimization (KDD 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eShaodan Zhai, Tian Xia, Shaojun Wang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=2623689\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGradient Boosted Feature Selection (KDD 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZhixiang Eddie Xu, Gao Huang, Kilian Q. Weinberger, Alice X. Zheng\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1901.04055\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dmlc/xgboost\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMulti-Class Deep Boosting (NIPS 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eVitaly Kuznetsov, Mehryar Mohri, Umar Syed\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/5514-multi-class-deep-boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDeconvolution of High Dimensional Mixtures via Boosting with Application to Diffusion-Weighted MRI of Human Brain (NIPS 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCharles Y. Zheng, Franco Pestilli, Ariel Rokem\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/5506-deconvolution-of-high-dimensional-mixtures-via-boosting-with-application-to-diffusion-weighted-mri-of-human-brain\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Drifting-Games Analysis for Online Learning and Applications to Boosting (NIPS 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHaipeng Luo, Robert E. Schapire\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1406.1856\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Boosting Framework on Grounds of Online Learning (NIPS 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTofigh Naghibi Mohamadpoor, Beat Pfister\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/5512-a-boosting-framework-on-grounds-of-online-learning.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGradient Boosting Factorization Machines (RECSYS 2014)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChen Cheng, Fen Xia, Tong Zhang, Irwin King, Michael R. Lyu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://tongzhang-ml.org/papers/recsys14-fm.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2013\u003c/h2\u003e\u003ca id=\"user-content-2013\" class=\"anchor\" aria-label=\"Permalink: 2013\" href=\"#2013\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Binary Keypoint Descriptors (CVPR 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTomasz Trzcinski, C. Mario Christoudias, Pascal Fua, Vincent Lepetit\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cvlab.epfl.ch/research/page-90554-en-html/research-detect-binboost/\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/biotrump/cvlab-BINBOOST\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePerturBoost: Practical Confidential Classifier Learning in the Cloud (ICDM 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKeke Chen, Shumin Guo\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/6729587\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMulticlass Semi-Supervised Boosting Using Similarity Learning (ICDM 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJafar Tanha, Mohammad Javad Saberian, Maarten van Someren\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cse.msu.edu/~rongjin/publications/MultiClass-08.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSaving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner (ICML 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePeng Sun, Jie Zhou\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v28/sun13.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGeneral Functional Matrix Factorization Using Gradient Boosting (ICML 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTianqi Chen, Hang Li, Qiang Yang, Yong Yu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://w.hangli-hl.com/uploads/3/1/6/8/3168008/icml_2013.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMargins, Shrinkage, and Boosting (ICML 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMatus Telgarsky\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1303.4172\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eQuickly Boosting Decision Trees - Pruning Underachieving Features Early (ICML 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRon Appel, Thomas J. Fuchs, Piotr Dollár, Pietro Perona\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v28/appel13.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pdollar/toolbox/blob/master/classify/adaBoostTrain.m\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eHuman Boosting (ICML 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHarsh H. Pareek, Pradeep Ravikumar\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cs.cmu.edu/~pradeepr/paperz/humanboosting.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCollaborative Boosting for Activity Classification in Microblogs (KDD 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYangqiu Song, Zhengdong Lu, Cane Wing-ki Leung, Qiang Yang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://chbrown.github.io/kdd-2013-usb/kdd/p482.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDirect 0-1 Loss Minimization and Margin Maximization with Boosting (NIPS 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eShaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/5214-direct-0-1-loss-minimization-and-margin-maximization-with-boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eReservoir Boosting : Between Online and Offline Ensemble Learning (NIPS 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLeonidas Lefakis, François Fleuret\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/5215-reservoir-boosting-between-online-and-offline-ensemble-learning\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNon-Linear Domain Adaptation with Boosting (NIPS 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCarlos J. Becker, C. Mario Christoudias, Pascal Fua\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/5200-non-linear-domain-adaptation-with-boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting in the Presence of Label Noise (UAI 2013)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJakramate Bootkrajang, Ata Kabán\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1309.6818\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2012\u003c/h2\u003e\u003ca id=\"user-content-2012\" class=\"anchor\" aria-label=\"Permalink: 2012\" href=\"#2012\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eContextual Boost for Pedestrian Detection (CVPR 2012)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYuanyuan Ding, Jing Xiao\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.5611\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eShrink Boost for Selecting Multi-LBP Histogram Features in Object Detection (CVPR 2012)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCher Keng Heng, Sumio Yokomitsu, Yuichi Matsumoto, Hajime Tamura\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/6248061\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Bottom-Up and Top-Down Visual Features for Saliency Estimation (CVPR 2012)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAli Borji\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://ilab.usc.edu/borji/papers/cvpr-2012-BUModel-v4.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Algorithms for Simultaneous Feature Extraction and Selection (CVPR 2012)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMohammad J. Saberian, Nuno Vasconcelos\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://svcl.ucsd.edu/publications/conference/2012/cvpr/SOPBoost.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSharing Features in Multi-class Boosting via Group Sparsity (CVPR 2012)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cs.adelaide.edu.au/~paulp/publications/pubs/sharing_cvpr2012.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFeature Weighting and Selection Using Hypothesis Margin of Boosting (ICDM 2012)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMalak Alshawabkeh, Javed A. Aslam, Jennifer G. Dy, David R. Kaeli\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.ece.neu.edu/fac-ece/jdy/papers/alshawabkeh-ICDM2012.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAn AdaBoost Algorithm for Multiclass Semi-supervised Learning (ICDM 2012)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJafar Tanha, Maarten van Someren, Hamideh Afsarmanesh\u003c/li\u003e\n\u003cli\u003e[[Paper]]\u003ca href=\"https://ieeexplore.ieee.org/document/6413799\" rel=\"nofollow\"\u003ehttps://ieeexplore.ieee.org/document/6413799\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem (ICML 2012)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePeng Sun, Mark D. Reid, Jie Zhou\u003c/li\u003e\n\u003cli\u003e[[Paper]](AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pengsun/AOSOLogitBoost\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAn Online Boosting Algorithm with Theoretical Justifications (ICML 2012)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eShang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1206.6422\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLearning Image Descriptors with the Boosting-Trick (NIPS 2012)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTomasz Trzcinski, C. Mario Christoudias, Vincent Lepetit, Pascal Fua\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/4848-learning-image-descriptors-with-the-boosting-trick.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/biotrump/cvlab-BINBOOST\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAccelerated Training for Matrix-norm Regularization: A Boosting Approach (NIPS 2012)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eXinhua Zhang, Yaoliang Yu, Dale Schuurmans\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/4663-accelerated-training-for-matrix-norm-regularization-a-boosting-approach\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLearning from Heterogeneous Sources via Gradient Boosting Consensus (SDM 2012)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eXiaoxiao Shi, Jean-François Paiement, David Grangier, Philip S. Yu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://david.grangier.info/papers/2012/shi_sdm_2012.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/PriyeshV/GBDT-CC\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2011\u003c/h2\u003e\u003ca id=\"user-content-2011\" class=\"anchor\" aria-label=\"Permalink: 2011\" href=\"#2011\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSelective Transfer Between Learning Tasks Using Task-Based Boosting (AAAI 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eEric Eaton, Marie desJardins\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cis.upenn.edu/~eeaton/papers/Eaton2011Selective.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eIncorporating Boosted Regression Trees into Ecological Latent Variable Models (AAAI 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/viewFile/3711/4086\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFlowBoost - Appearance Learning from Sparsely Annotated Video (CVPR 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKarim Ali, David Hasler, François Fleuret\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.karimali.org/publications/AHF_CVPR11.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdaBoost on Low-Rank PSD Matrices for Metric Learning (CVPR 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJinbo Bi, Dijia Wu, Le Lu, Meizhu Liu, Yimo Tao, Matthias Wolf\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026amp;arnumber=5995363\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Local Structured HOG-LBP for Object Localization (CVPR 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJunge Zhang, Kaiqi Huang, Yinan Yu, Tieniu Tan\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cbsr.ia.ac.cn/users/ynyu/1682.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Direct Formulation for Totally-Corrective Multi-Class Boosting (CVPR 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChunhua Shen, Zhihui Hao\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5995554\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGated Classifiers: Boosting Under High Intra-class Variation (CVPR 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eOscar M. Danielsson, Babak Rasolzadeh, Stefan Carlsson\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.nada.kth.se/cvap/cvg/papers/danielssonCVPR11.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTaylorBoost: First and Second-order Boosting Algorithms with Explicit Margin Control (CVPR 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMohammad J. Saberian, Hamed Masnadi-Shirazi, Nuno Vasconcelos\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/5995605\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pythonhosted.org/bob.learn.boosting/\" rel=\"nofollow\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRobust and Efficient Regularized Boosting Using Total Bregman Divergence (CVPR 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMeizhu Liu, Baba C. Vemuri\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/5995686\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTreat Samples differently: Object Tracking with Semi-Supervised Online CovBoost (ICCV 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGuorong Li, Lei Qin, Qingming Huang, Junbiao Pang, Shuqiang Jiang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/6126297\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLinkBoost: A Novel Cost-Sensitive Boosting Framework for Community-Level Network Link Prediction (ICDM 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePrakash Mandayam Comar, Pang-Ning Tan, Anil K. Jain\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cse.msu.edu/~ptan/papers/icdm2011.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLearning Markov Logic Networks via Functional Gradient Boosting (ICDM 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/starling-lab/BoostSRL\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/6137236\" rel=\"nofollow\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting on a Budget: Sampling for Feature-Efficient Prediction (ICML 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLev Reyzin\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.icml-2011.org/papers/348_icmlpaper.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMulticlass Boosting with Hinge Loss based on Output Coding (ICML 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTianshi Gao, Daphne Koller\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://ai.stanford.edu/~tianshig/papers/multiclassHingeBoost-ICML2011.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/memect/hao/blob/master/awesome/multiclass-boosting.md\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGeneralized Boosting Algorithms for Convex Optimization (ICML 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlexander Grubb, Drew Bagnell\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/pdf/1105.2054.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eImitation Learning in Relational Domains: A Functional-Gradient Boosting Approach (IJCAI 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://ftp.cs.wisc.edu/machine-learning/shavlik-group/natarajan.ijcai11.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting with Maximum Adaptive Sampling (NIPS 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCharles Dubout, François Fleuret\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/4310-boosting-with-maximum-adaptive-sampling\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eThe Fast Convergence of Boosting (NIPS 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMatus Telgarsky\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/4343-the-fast-convergence-of-boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eShareBoost: Efficient Multiclass Learning with Feature Sharing (NIPS 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eShai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/4213-shareboost-efficient-multiclass-learning-with-feature-sharing\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMulticlass Boosting: Theory and Algorithms (NIPS 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMohammad J. Saberian, Nuno Vasconcelos\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/4450-multiclass-boosting-theory-and-algorithms.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eVariance Penalizing AdaBoost (NIPS 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePannagadatta K. Shivaswamy, Tony Jebara\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/4207-variance-penalizing-adaboost.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMKBoost: A Framework of Multiple Kernel Boosting (SDM 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHao Xia, Steven C. H. Hoi\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3280\u0026amp;context=sis_research\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Boosting Approach to Improving Pseudo-Relevance Feedback (SIGIR 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYuanhua Lv, ChengXiang Zhai, Wan Chen\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.tyr.unlu.edu.ar/tallerIR/2012/papers/pseudorelevance.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBagging Gradient-Boosted Trees for High Precision, Low Variance Ranking Models (SIGIR 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYasser Ganjisaffar, Rich Caruana, Cristina Videira Lopes\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/materials/bagging_lmbamart_jforests.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting as a Product of Experts (UAI 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNarayanan Unny Edakunni, Gary Brown, Tim Kovacs\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1202.3716\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eParallel Boosted Regression Trees for Web Search Ranking (WWW 2011)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eStephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, Jennifer Paykin\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cs.cornell.edu/~kilian/papers/fr819-tyreeA.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/YS-L/pgbm\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2010\u003c/h2\u003e\u003ca id=\"user-content-2010\" class=\"anchor\" aria-label=\"Permalink: 2010\" href=\"#2010\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eThe Boosting Effect of Exploratory Behaviors (AAAI 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJivko Sinapov, Alexander Stoytchev\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/download/1777/2265\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting-Based System Combination for Machine Translation (ACL 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTong Xiao, Jingbo Zhu, Muhua Zhu, Huizhen Wang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aclweb.org/anthology/P10-1076\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBagBoo: A Scalable Hybrid Bagging-the-Boosting Model (CIKM 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDmitry Yurievich Pavlov, Alexey Gorodilov, Cliff A. Brunk\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://cache-default03h.cdn.yandex.net/download.yandex.ru/company/a_scalable_hybrid_bagging_the_boosting_model.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/arogozhnikov/infiniteboost\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAutomatic Detection of Craters in Planetary Images: an Embedded Framework Using Feature Selection and Boosting (CIKM 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWei Ding, Tomasz F. Stepinski, Lourenço P. C. Bandeira, Ricardo Vilalta, Youxi Wu, Zhenyu Lu, Tianyu Cao\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.uh.edu/~rvilalta/papers/2010/cikm10.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFacial Point Detection Using Boosted Regression and Graph Models (CVPR 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMichel François Valstar, Brais Martínez, Xavier Binefa, Maja Pantic\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ibug.doc.ic.ac.uk/media/uploads/documents/CVPR-2010-ValstarEtAl-CAMERA.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting for Transfer Learning with Multiple Sources (CVPR 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYi Yao, Gianfranco Doretto\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/5539857\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEfficient Rotation Invariant Object Detection Using Boosted Random Ferns (CVPR 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMichael Villamizar, Francesc Moreno-Noguer, Juan Andrade-Cetto, Alberto Sanfeliu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.307.4002\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eImplicit Hierarchical Boosting for Multi-view Object Detection (CVPR 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eXavier Perrotton, Marc Sturzel, Michel Roux\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/5540115\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn-Line Semi-Supervised Multiple-Instance Boosting (CVPR 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBernhard Zeisl, Christian Leistner, Amir Saffari, Horst Bischof\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/5539860\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOnline Multi-Class LPBoost (CVPR 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAmir Saffari, Martin Godec, Thomas Pock, Christian Leistner, Horst Bischof\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.165.8939\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/amirsaffari/online-multiclass-lpboost\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eHomotopy Regularization for Boosting (ICDM 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZheng Wang, Yangqiu Song, Changshui Zhang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/5694094\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eExploiting Local Data Uncertainty to Boost Global Outlier Detection (ICDM 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBo Liu, Jie Yin, Yanshan Xiao, Longbing Cao, Philip S. Yu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/5693984\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Classifiers with Tightened L0-Relaxation Penalties (ICML 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNoam Goldberg, Jonathan Eckstein\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/11df/aed4ec2a2f72878789fa3a54d588d693bdda.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting for Regression Transfer (ICML 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDavid Pardoe, Peter Stone\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cs.utexas.edu/~dpardoe/papers/ICML10.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jay15summer/Two-stage-TrAdaboost.R2\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Backpropagation Learning for Training Deep Modular Networks (ICML 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlexander Grubb, J. Andrew Bagnell\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://icml.cc/Conferences/2010/papers/451.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFast Boosting Using Adversarial Bandits (ICML 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRóbert Busa-Fekete, Balázs Kégl\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.lri.fr/~kegl/research/PDFs/BuKe10.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting with Structure Information in the Functional Space: an Application to Graph Classification (KDD 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHongliang Fei, Jun Huan\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=1835804.1835886\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMulti-task Learning for Boosting with Application to Web Search Ranking (KDD 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eOlivier Chapelle, Pannagadatta K. Shivaswamy, Srinivas Vadrevu, Kilian Q. Weinberger, Ya Zhang, Belle L. Tseng\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=1835953\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Theory of Multiclass Boosting (NIPS 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIndraneel Mukherjee, Robert E. Schapire\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://rob.schapire.net/papers/multiboost-journal.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Classifier Cascades (NIPS 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMohammad J. Saberian, Nuno Vasconcelos\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/4033-boosting-classifier-cascades.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eJoint Cascade Optimization Using A Product Of Boosted Classifiers (NIPS 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLeonidas Lefakis, François Fleuret\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/4148-joint-cascade-optimization-using-a-product-of-boosted-classifiers\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRobust LogitBoost and Adaptive Base Class (ABC) LogitBoost (UAI 2010)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePing Li\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1203.3491\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pengsun/AOSOLogitBoost\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2009\u003c/h2\u003e\u003ca id=\"user-content-2009\" class=\"anchor\" aria-label=\"Permalink: 2009\" href=\"#2009\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFeature Selection for Ranking Using Boosted Trees (CIKM 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFeng Pan, Tim Converse, David Ahn, Franco Salvetti, Gianluca Donato\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.francosalvetti.com/cikm09_camera2.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting KNN Text Classification Accuracy by Using Supervised Term Weighting Schemes (CIKM 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIyad Batal, Milos Hauskrecht\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://people.cs.pitt.edu/~milos/research/CIKM_2009_boosting_KNN.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eStochastic Gradient Boosted Distributed Decision Trees (CIKM 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJerry Ye, Jyh-Herng Chow, Jiang Chen, Zhaohui Zheng\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://cse.iitrpr.ac.in/ckn/courses/f2012/thomas.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA General Magnitude-Preserving Boosting Algorithm for Search Ranking (CIKM 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChenguang Zhu, Weizhu Chen, Zeyuan Allen Zhu, Gang Wang, Dong Wang, Zheng Chen\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/cikm2009-1.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eReducing Joint Boost-Based Multiclass Classification to Proximity Search (CVPR 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlexandra Stefan, Vassilis Athitsos, Quan Yuan, Stan Sclaroff\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.semanticscholar.org/paper/Reducing-JointBoost-based-multiclass-classification-Stefan-Athitsos/08ba1a7d91ce9b4ac26869bfe4bb7c955b0d1a24\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eImbalanced RankBoost for Efficiently Ranking Large-Scale Image-Video Collections (CVPR 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMichele Merler, Rong Yan, John R. Smith\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.semanticscholar.org/paper/Imbalanced-RankBoost-for-efficiently-ranking-Merler-Yan/031ba6bf0d6df8bd3aa686ce85791b7d74f0b6d5\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRegularized Multi-Class Semi-Supervised Boosting (CVPR 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAmir Saffari, Christian Leistner, Horst Bischof\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/abstract/document/5206715\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLearning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene (CVPR 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYuan Li, Chang Huang, Ram Nevatia\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.8335\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Multi-task Learning for Face Verification with Applications to Web Image and Video Search (CVPR 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eXiaogang Wang, Cha Zhang, Zhengyou Zhang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.ee.cuhk.edu.hk/~xgwang/webface.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLidarBoost: Depth Superresolution for ToF 3D Shape Scanning (CVPR 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSebastian Schuon, Christian Theobalt, James E. Davis, Sebastian Thrun\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://ai.stanford.edu/~schuon/sr/cvpr09_poster_lidarboost.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eModel Adaptation via Model Interpolation and Boosting for Web Search Ranking (EMNLP 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJianfeng Gao, Qiang Wu, Chris Burges, Krysta Marie Svore, Yi Su, Nazan Khan, Shalin Shah, Hongyan Zhou\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/7a82/66335d0b44596574588eabb090bfeae4ab35.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFinding Shareable Informative Patterns and Optimal Coding Matrix for Multiclass Boosting (ICCV 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBang Zhang, Getian Ye, Yang Wang, Jie Xu, Gunawan Herman\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/5459146\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRankBoost with L1 Regularization for Facial Expression Recognition and Intensity Estimation (ICCV 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePeng Yang, Qingshan Liu, Dimitris N. Metaxas\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/5459371\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Robust Boosting Tracker with Minimum Error Bound in a Co-Training Framework (ICCV 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRong Liu, Jian Cheng, Hanqing Lu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://nlpr-web.ia.ac.cn/2009papers/gjhy/gh1.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTutorial Summary: Survey of Boosting from an Optimization Perspective (ICML 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eManfred K. Warmuth, S. V. N. Vishwanathan\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.stat.purdue.edu/~vishy/erlpboost/manfred.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Products of Base Classifiers (ICML 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBalázs Kégl, Róbert Busa-Fekete\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://users.lal.in2p3.fr/kegl/research/PDFs/keglBusafekete09.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eABC-boost: Adaptive Base Class Boost for Multi-Class Classification (ICML 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePing Li\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://icml.cc/Conferences/2009/papers/417.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting with Structural Sparsity (ICML 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJohn C. Duchi, Yoram Singer\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://web.stanford.edu/~jduchi/projects/DuchiSi09a.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Constrained Mutual Subspace Method for Robust Image-Set Based Object Recognition (IJCAI 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eXi Li, Kazuhiro Fukui, Nanning Zheng\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/220812439_Boosting_Constrained_Mutual_Subspace_Method_for_Robust_Image-Set_Based_Object_Recognition\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eInformation Theoretic Regularization for Semi-supervised Boosting (KDD 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLei Zheng, Shaojun Wang, Yan Liu, Chi-Hoon Lee\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/5255/242d50851ce56354e10ae8fdcee6f47591c9.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePotential-Based Agnostic Boosting (NIPS 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAdam Kalai, Varun Kanade\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/3676-potential-based-agnostic-boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePositive Semidefinite Metric Learning with Boosting (NIPS 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/3658-positive-semidefinite-metric-learning-with-boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting with Spatial Regularization (NIPS 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZhen James Xiang, Yongxin Taylor Xi, Uri Hasson, Peter J. Ramadge\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/3696-boosting-with-spatial-regularization\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEffective Boosting of Na%C3%AFve Bayesian Classifiers by Local Accuracy Estimation (PAKDD 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZhipeng Xie\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/978-3-642-01307-2_88\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMulti-resolution Boosting for Classification and Regression Problems (PAKDD 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChandan K. Reddy, Jin Hyeong Park\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://dmkd.cs.vt.edu/papers/PAKDD09.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEfficient Active Learning with Boosting (SDM 2009)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZheng Wang, Yangqiu Song, Changshui Zhang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/c8be/b70c37e4b4c4ad77e46b39060c977779d201.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2008\u003c/h2\u003e\u003ca id=\"user-content-2008\" class=\"anchor\" aria-label=\"Permalink: 2008\" href=\"#2008\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGroup-Based Learning: A Boosting Approach (CIKM 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWeijian Ni, Jun Xu, Hang Li, Yalou Huang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.bigdatalab.ac.cn/~junxu/publications/CIKM2008_GroupLearn.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSemi-Supervised Boosting Using Visual Similarity Learning (CVPR 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChristian Leistner, Helmut Grabner, Horst Bischof\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.144.7914\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMining Compositional Features for Boosting (CVPR 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJunsong Yuan, Jiebo Luo, Ying Wu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4587347\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Deformable Model for Human Body Alignment (CVPR 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eXiaoming Liu, Ting Yu, Thomas Sebastian, Peter H. Tu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cse.msu.edu/~liuxm/publication/Liu_Yu_Sebastian_Tu_cvpr08.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDiscriminative Modeling by Boosting on Multilevel Aggregates (CVPR 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJason J. Corso\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.3166\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFace Alignment via Boosted Ranking Model (CVPR 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHao Wu, Xiaoming Liu, Gianfranco Doretto\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/4587753\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Adaptive Linear Weak Classifiers for Online Learning and Tracking (CVPR 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eToufiq Parag, Fatih Porikli, Ahmed M. Elgammal\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.merl.com/publications/docs/TR2008-065.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDetection with Multi-Exit Asymmetric Boosting (CVPR 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMinh-Tri Pham, V-D. D. Hoang, Tat-Jen Cham\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.330.6364\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Ordinal Features for Accurate and Fast Iris Recognition (CVPR 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZhaofeng He, Zhenan Sun, Tieniu Tan, Xianchao Qiu, Cheng Zhong, Wenbo Dong\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/224323296_Boosting_ordinal_features_for_accurate_and_fast_iris_recognition\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdaptive and Compact Shape Descriptor by Progressive Feature Combination and Selection with Boosting (CVPR 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCheng Chen, Yueting Zhuang, Jun Xiao, Fei Wu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/4587613\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Relational Sequence Alignments (ICDM 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAndreas Karwath, Kristian Kersting, Niels Landwehr\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cs.uni-potsdam.de/~landwehr/ICDM08boosting.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting with Incomplete Information (ICML 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGholamreza Haffari, Yang Wang, Shaojun Wang, Greg Mori, Feng Jiao\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://users.monash.edu.au/~gholamrh/publications/boosting_icml08_slides.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eManifoldBoost: Stagewise Function Approximation for Fully-, Semi- and Un-supervised Learning (ICML 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNicolas Loeff, David A. Forsyth, Deepak Ramachandran\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://reason.cs.uiuc.edu/deepak/manifoldboost.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRandom Classification Noise Defeats All Convex Potential Boosters (ICML 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePhilip M. Long, Rocco A. Servedio\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://phillong.info/publications/LS09_potential.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMulti-class Cost-Sensitive Boosting with P-norm Loss Functions (KDD 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAurelie C. Lozano, Naoki Abe\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=1401953\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features (NIPS 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTae-Kyun Kim, Roberto Cipolla\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/3483-mcboost-multiple-classifier-boosting-for-perceptual-co-clustering-of-images-and-visual-features\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning (NIPS 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChunhua Shen, Alan Welsh, Lei Wang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.879.7750\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn the Design of Loss Functions for Classification: Theory, Robustness to Outliers, and SavageBoost (NIPS 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHamed Masnadi-Shirazi, Nuno Vasconcelos\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/3591-on-the-design-of-loss-functions-for-classification-theory-robustness-to-outliers-and-savageboost\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdaptive Martingale Boosting (NIPS 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePhilip M. Long, Rocco A. Servedio\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://phillong.info/publications/LS08_adaptive_martingale_boosting.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Boosting Algorithm for Learning Bipartite Ranking Functions with Partially Labeled Data (SIGIR 2008)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMassih-Reza Amini, Tuong-Vinh Truong, Cyril Goutte\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://ama.liglab.fr/~amini/Publis/SemiSupRanking_sigir08.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2007\u003c/h2\u003e\u003ca id=\"user-content-2007\" class=\"anchor\" aria-label=\"Permalink: 2007\" href=\"#2007\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUsing Error-Correcting Output Codes with Model-Refinement to Boost Centroid Text Classifier (ACL 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSongbo Tan\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=1557794\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFast Human Pose Estimation using Appearance and Motion via Multi-Dimensional Boosting Regression (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlessandro Bissacco, Ming-Hsuan Yang, Stefano Soatto\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://vision.ucla.edu/papers/bissaccoYS07.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGeneric Face Alignment using Boosted Appearance Model (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eXiaoming Liu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4270290\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEigenboosting: Combining Discriminative and Generative Information (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHelmut Grabner, Peter M. Roth, Horst Bischof\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.tugraz.at/fileadmin/user_upload/Institute/ICG/Documents/lrs/pubs/grabner_cvpr_07.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOnline Learning Asymmetric Boosted Classifiers for Object Detection (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMinh-Tri Pham, Tat-Jen Cham\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/abstract/document/4270108\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eImproving Part based Object Detection by Unsupervised Online Boosting (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBo Wu, Ram Nevatia\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/4270173\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Specialized Processor Suitable for AdaBoost-Based Detection with Haar-like Features (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMasayuki Hiromoto, Kentaro Nakahara, Hiroki Sugano, Yukihiro Nakamura, Ryusuke Miyamoto\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/4270413\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSimultaneous Object Detection and Segmentation by Boosting Local Shape Feature based Classifier (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBo Wu, Ram Nevatia\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.9795\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCompositional Boosting for Computing Hierarchical Image Structures (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTianfu Wu, Gui-Song Xia, Song Chun Zhu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/4270059\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Coded Dynamic Features for Facial Action Units and Facial Expression Recognition (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePeng Yang, Qingshan Liu, Dimitris N. Metaxas\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/4270084\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eObject Classification in Visual Surveillance Using Adaboost (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJohn-Paul Renno, Dimitrios Makris, Graeme A. Jones\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/abstract/document/4270512\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Boosting Regression Approach to Medical Anatomy Detection (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eShaohua Kevin Zhou, Jinghao Zhou, Dorin Comaniciu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://ww.w.comaniciu.net/Papers/BoostingRegression_CVPR07.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eJoint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJingdan Zhang, Shaohua Kevin Zhou, Leonard McMillan, Dorin Comaniciu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://csbio.unc.edu/mcmillan/pubs/CVPR07_Zhang.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKernel Sharing With Joint Boosting For Multi-Class Concept Detection (CVPR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWei Jiang, Shih-Fu Chang, Alexander C. Loui\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.ee.columbia.edu/~wjiang/references/jiangcvprws07.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eScale-Space Based Weak Regressors for Boosting (ECML 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJin Hyeong Park, Chandan K. Reddy\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cs.wayne.edu/~reddy/Papers/ECML07.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAvoiding Boosting Overfitting by Removing Confusing Samples (ECML 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlexander Vezhnevets, Olga Barinova\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://groups.inf.ed.ac.uk/calvin/hp_avezhnev/Pubs/AvoidingBoostingOverfitting.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDynamicBoost: Boosting Time Series Generated by Dynamical Systems (ICCV 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRené Vidal, Paolo Favaro\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://vision.jhu.edu/assets/VidalICCV07.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eIncremental Learning of Boosted Face Detector (ICCV 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChang Huang, Haizhou Ai, Takayoshi Yamashita, Shihong Lao, Masato Kawade\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.126.9012\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGradient Feature Selection for Online Boosting (ICCV 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eXiaoming Liu, Ting Yu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cse.msu.edu/~liuxm/publication/Liu_Yu_ICCV2007.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFast Training and Selection of Haar Features Using Statistics in Boosting-based Face Detection (ICCV 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMinh-Tri Pham, Tat-Jen Cham\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.212.6173\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCluster Boosted Tree Classifier for Multi-View - Multi-Pose Object Detection (ICCV 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBo Wu, Ramakant Nevatia\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.9885\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAsymmetric Boosting (ICML 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHamed Masnadi-Shirazi, Nuno Vasconcelos\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.svcl.ucsd.edu/publications/conference/2007/icml07/AsymmetricBoosting.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting for Transfer Learning (ICML 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cs.ust.hk/~qyang/Docs/2007/tradaboost.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGradient Boosting for Kernelized Output Spaces (ICML 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePierre Geurts, Louis Wehenkel, Florence d'Alché-Buc\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.435.3970\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting a Complete Technique to Find MSS and MUS Thanks to a Local Search Oracle (IJCAI 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eÉric Grégoire, Bertrand Mazure, Cédric Piette\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cril.univ-artois.fr/~piette/IJCAI07_HYCAM.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTraining Conditional Random Fields Using Virtual Evidence Boosting (IJCAI 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLin Liao, Tanzeem Choudhury, Dieter Fox, Henry A. Kautz\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/Proceedings/07/Papers/407.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSimple Training of Dependency Parsers via Structured Boosting (IJCAI 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eQin Iris Wang, Dekang Lin, Dale Schuurmans\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/Proceedings/07/Papers/284.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eReal Boosting a la Carte with an Application to Boosting Oblique Decision Tree (IJCAI 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eClaudia Henry, Richard Nock, Frank Nielsen\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/Proceedings/07/Papers/135.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eManaging Domain Knowledge and Multiple Models with Boosting (IJCAI 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePeng Zang, Charles Lee Isbell Jr.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ijcai.org/Proceedings/07/Papers/185.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eModel-Shared Subspace Boosting for Multi-label Classification (KDD 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRong Yan, Jelena Tesic, John R. Smith\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://rogerioferis.com/VisualRecognitionAndSearch2014/material/papers/IMARSKDD2007.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRegularized Boost for Semi-Supervised Learning (NIPS 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKe Chen, Shihai Wang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/3167-regularized-boost-for-semi-supervised-learning.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Algorithms for Maximizing the Soft Margin (NIPS 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eManfred K. Warmuth, Karen A. Glocer, Gunnar Rätsch\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/3374-boosting-algorithms-for-maximizing-the-soft-margin.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMcRank: Learning to Rank Using Multiple Classification and Gradient Boosting (NIPS 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePing Li, Christopher J. C. Burges, Qiang Wu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/3270-mcrank-learning-to-rank-using-multiple-classification-and-gradient-boosting.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOne-Pass Boosting (NIPS 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZafer Barutçuoglu, Philip M. Long, Rocco A. Servedio\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://phillong.info/publications/BLS07_one_pass.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting the Area under the ROC Curve (NIPS 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePhilip M. Long, Rocco A. Servedio\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/3247-boosting-the-area-under-the-roc-curve.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFilterBoost: Regression and Classification on Large Datasets (NIPS 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJoseph K. Bradley, Robert E. Schapire\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://rob.schapire.net/papers/FilterBoost_paper.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA General Boosting Method and its Application to Learning Ranking Functions for Web Search (NIPS 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/8f8d/874a3f0217289ba317b1f6175ac3b6f73d70.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEfficient Multiclass Boosting Classification with Active Learning (SDM 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJian Huang, Seyda Ertekin, Yang Song, Hongyuan Zha, C. Lee Giles\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://epubs.siam.org/doi/abs/10.1137/1.9781611972771.27\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdaRank: a Boosting Algorithm for Information Retrieval (SIGIR 2007)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJun Xu, Hang Li\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.bigdatalab.ac.cn/~junxu/publications/SIGIR2007_AdaRank.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2006\u003c/h2\u003e\u003ca id=\"user-content-2006\" class=\"anchor\" aria-label=\"Permalink: 2006\" href=\"#2006\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGradient Boosting for Sequence Alignment (AAAI 2006)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCharles Parker, Alan Fern, Prasad Tadepalli\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://web.engr.oregonstate.edu/~afern/papers/aaai06-align.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Kernel Models for Regression (ICDM 2006)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePing Sun, Xin Yao\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cs.bham.ac.uk/~xin/papers/icdm06SunYao.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting for Learning Multiple Classes with Imbalanced Class Distribution (ICDM 2006)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYanmin Sun, Mohamed S. Kamel, Yang Wang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://people.ee.duke.edu/~lcarin/ImbalancedClassDistribution.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting the Feature Space: Text Classification for Unstructured Data on the Web (ICDM 2006)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYang Song, Ding Zhou, Jian Huang, Isaac G. Councill, Hongyuan Zha, C. Lee Giles\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://sonyis.me/paperpdf/icdm06_song.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTotally Corrective Boosting Algorithms that Maximize the Margin (ICML 2006)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eManfred K. Warmuth, Jun Liao, Gunnar Rätsch\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://users.soe.ucsc.edu/~manfred/pubs/C75.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eHow Boosting the Margin Can Also Boost Classifier Complexity (ICML 2006)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLev Reyzin, Robert E. Schapire\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://rob.schapire.net/papers/boost_complexity.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMulticlass Boosting with Repartitioning (ICML 2006)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLing Li\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://authors.library.caltech.edu/72259/1/p569-li.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdaBoost is Consistent (NIPS 2006)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePeter L. Bartlett, Mikhail Traskin\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://jmlr.csail.mit.edu/papers/volume8/bartlett07b/bartlett07b.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Structured Prediction for Imitation Learning (NIPS 2006)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNathan D. Ratliff, David M. Bradley, J. Andrew Bagnell, Joel E. Chestnutt\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/3154-boosting-structured-prediction-for-imitation-learning.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eChained Boosting (NIPS 2006)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChristian R. Shelton, Wesley Huie, Kin Fai Kan\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/2981-chained-boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eWhen Efficient Model Averaging Out-Performs Boosting and Bagging (PKDD 2006)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIan Davidson, Wei Fan\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/11871637_46\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2005\u003c/h2\u003e\u003ca id=\"user-content-2005\" class=\"anchor\" aria-label=\"Permalink: 2005\" href=\"#2005\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSemantic Place Classification of Indoor Environments with Mobile Robots Using Boosting (AAAI 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAxel Rottmann, Óscar Martínez Mozos, Cyrill Stachniss, Wolfram Burgard\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www2.informatik.uni-freiburg.de/~stachnis/pdf/rottmann05aaai.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting-based Parse Reranking with Subtree Features (ACL 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTaku Kudo, Jun Suzuki, Hideki Isozaki\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://chasen.org/~taku/publications/acl2005.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUsing RankBoost to Compare Retrieval Systems (CIKM 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHuyen-Trang Vu, Patrick Gallinari\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.98.9470\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eClassifier Fusion Using Shared Sampling Distribution for Boosting (ICDM 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCostin Barbu, Raja Tanveer Iqbal, Jing Peng\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/1565659\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSemi-Supervised Mixture of Kernels via LPBoost Methods (ICDM 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJinbo Bi, Glenn Fung, Murat Dundar, R. Bharat Rao\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/1565728\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEfficient Discriminative Learning of Bayesian Network Classifier via Boosted Augmented Naive Bayes (ICML 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYushi Jing, Vladimir Pavlovic, James M. Rehg\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://mrl.isr.uc.pt/pub/bscw.cgi/d27355/Jing05Efficient.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUnifying the Error-Correcting and Output-Code AdaBoost within the Margin Framework (ICML 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYijun Sun, Sinisa Todorovic, Jian Li, Dapeng Wu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.138.4246\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Smoothed Boosting Algorithm Using Probabilistic Output Codes (ICML 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRong Jin, Jian Zhang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.stat.purdue.edu/~jianzhan/papers/icml05jin.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRobust Boosting and its Relation to Bagging (KDD 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSaharon Rosset\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.tau.ac.il/~saharon/papers/bagboost.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEfficient Computations via Scalable Sparse Kernel Partial Least Squares and Boosted Latent Features (KDD 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMichinari Momma\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.387.2078\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMultiple Instance Boosting for Object Detection (NIPS 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePaul A. Viola, John C. Platt, Cha Zhang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.138.8312\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eConvergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations (NIPS 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAurelie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cs.princeton.edu/~schapire/papers/betamix.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted decision trees for word recognition in handwritten document retrieval (SIGIR 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNicholas R. Howe, Toni M. Rath, R. Manmatha\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.152.1551\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eObtaining Calibrated Probabilities from Boosting (UAI 2005)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlexandru Niculescu-Mizil, Rich Caruana\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cs.cornell.edu/~caruana/niculescu.scldbst.crc.rev4.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2004\u003c/h2\u003e\u003ca id=\"user-content-2004\" class=\"anchor\" aria-label=\"Permalink: 2004\" href=\"#2004\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOnline Parallel Boosting (AAAI 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJesse A. Reichler, Harlan D. Harris, Michael A. Savchenko\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aaai.org/Papers/AAAI/2004/AAAI04-059.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Boosting Approach to Multiple Instance Learning (ECML 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePeter Auer, Ronald Ortner\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/978-3-540-30115-8_9\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Boosting Algorithm for Classification of Semi-Structured Text (EMNLP 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTaku Kudo, Yuji Matsumoto\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aclweb.org/anthology/W04-3239\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eText Classification by Boosting Weak Learners based on Terms and Concepts (ICDM 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eStephan Bloehdorn, Andreas Hotho\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/1410303\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Grammatical Inference with Confidence Oracles (ICML 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www1.univ-ag.fr/~rnock/Articles/Drafts/icml04-jnss.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSurrogate Maximization/Minimization Algorithms for AdaBoost and the Logistic Regression Model (ICML 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZhihua Zhang, James T. Kwok, Dit-Yan Yeung\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://icml.cc/Conferences/2004/proceedings/papers/77.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTraining Conditional Random Fields via Gradient Tree Boosting (ICML 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://web.engr.oregonstate.edu/~tgd/publications/ml2004-treecrf.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Margin Based Distance Functions for Clustering (ICML 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTomer Hertz, Aharon Bar-Hillel, Daphna Weinshall\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cs.huji.ac.il/~daphna/papers/distboost-icml.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eColumn-Generation Boosting Methods for Mixture of Kernels (KDD 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJinbo Bi, Tong Zhang, Kristin P. Bennett\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.6359\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOptimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging (NIPS 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eVladimir Koltchinskii, Manel Martínez-Ramón, Stefan Posse\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/2699-optimal-aggregation-of-classifiers-and-boosting-maps-in-functional-magnetic-resonance-imaging.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting on Manifolds: Adaptive Regularization of Base Classifiers (NIPS 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBalázs Kégl, Ligen Wang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/2613-boosting-on-manifolds-adaptive-regularization-of-base-classifiers\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eContextual Models for Object Detection Using Boosted Random Fields (NIPS 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAntonio Torralba, Kevin P. Murphy, William T. Freeman\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cs.ubc.ca/~murphyk/Papers/BRF-nips04-camera.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGeneralization Error and Algorithmic Convergence of Median Boosting (NIPS 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBalázs Kégl\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.70.8990\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAn Application of Boosting to Graph Classification (NIPS 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTaku Kudo, Eisaku Maeda, Yuji Matsumoto\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/2739-an-application-of-boosting-to-graph-classification\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLogistic Regression and Boosting for Labeled Bags of Instances (PAKDD 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eXin Xu, Eibe Frank\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cs.waikato.ac.nz/~ml/publications/2004/xu-frank.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFast and Light Boosting for Adaptive Mining of Data Streams (PAKDD 2004)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFang Chu, Carlo Zaniolo\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://web.cs.ucla.edu/~zaniolo/papers/NBCAJMW77MW0J8CP.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2003\u003c/h2\u003e\u003ca id=\"user-content-2003\" class=\"anchor\" aria-label=\"Permalink: 2003\" href=\"#2003\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn Boosting and the Exponential Loss (AISTATS 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAbraham J. Wyner\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www-stat.wharton.upenn.edu/~ajw/exploss.ps\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Support Vector Machines for Text Classification through Parameter-Free Threshold Relaxation (CIKM 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJames G. Shanahan, Norbert Roma\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=956911\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLearning Cross-Document Structural Relationships Using Boosting (CIKM 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZhu Zhang, Jahna Otterbacher, Dragomir R. Radev\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.128.7712\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn Boosting Improvement: Error Reduction and Convergence Speed-Up (ECML 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMarc Sebban, Henri-Maxime Suchier\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/978-3-540-39857-8_32\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Lazy Decision Trees (ICML 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eXiaoli Zhang Fern, Carla E. Brodley\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aaai.org/Papers/ICML/2003/ICML03-026.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn the Convergence of Boosting Procedures (ICML 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTong Zhang, Bin Yu\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/dd3f/901b232280533fbdb9e57f144f44723617cf.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLinear Programming Boosting for Uneven Datasets (ICML 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJure Leskovec, John Shawe-Taylor\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cs.stanford.edu/people/jure/pubs/textbooster-icml03.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMonte Carlo Theory as an Explanation of Bagging and Boosting (IJCAI 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRoberto Esposito, Lorenza Saitta\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=1630733\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn the Dynamics of Boosting (NIPS 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCynthia Rudin, Ingrid Daubechies, Robert E. Schapire\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/2535-on-the-dynamics-of-boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMutual Boosting for Contextual Inference (NIPS 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMichael Fink, Pietro Perona\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/2520-mutual-boosting-for-contextual-inference\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Versus Covering (NIPS 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKohei Hatano, Manfred K. Warmuth\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/2532-boosting-versus-covering\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMultiple-Instance Learning via Disjunctive Programming Boosting (NIPS 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eStuart Andrews, Thomas Hofmann\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/2478-multiple-instance-learning-via-disjunctive-programming-boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAveraged Boosting: A Noise-Robust Ensemble Method (PAKDD 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYongdai Kim\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/3-540-36175-8_38\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSMOTEBoost: Improving Prediction of the Minority Class in Boosting (PKDD 2003)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, Kevin W. Bowyer\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www3.nd.edu/~nchawla/papers/ECML03.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2002\u003c/h2\u003e\u003ca id=\"user-content-2002\" class=\"anchor\" aria-label=\"Permalink: 2002\" href=\"#2002\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMinimum Majority Classification and Boosting (AAAI 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePhilip M. Long\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://phillong.info/publications/minmaj.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRanking Algorithms for Named Entity Extraction: Boosting and the Voted Perceptron (ACL 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMichael Collins\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aclweb.org/anthology/P02-1062\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting to Correct Inductive Bias in Text Classification (CIKM 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYan Liu, Yiming Yang, Jaime G. Carbonell\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=584792.584850\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eHow to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code (ECML 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGünther Eibl, Karl Peter Pfeiffer\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=650068\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eScaling Boosting by Margin-Based Inclusionof Features and Relations (ECML 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSusanne Hoche, Stefan Wrobel\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/3-540-36755-1_13\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Robust Boosting Algorithm (ECML 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRichard Nock, Patrice Lefaucheur\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=650081\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eiBoost: Boosting Using an instance-Based Exponential Weighting Scheme (ECML 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eStephen Kwek, Chau Nguyen\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/220516082_iBoost_Boosting_using_an_instance-based_exponential_weighting_scheme\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Density Function Estimators (ECML 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFranck Thollard, Marc Sebban, Philippe Ézéquel\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007%2F3-540-36755-1_36\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eStatistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond (ICML 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTong Zhang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/221344927_Statistical_Behavior_and_Consistency_of_Support_Vector_Machines_Boosting_and_Beyond\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Boosted Maximum Entropy Model for Learning Text Chunking (ICML 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSeong-Bae Park, Byoung-Tak Zhang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/221345636_A_Boosted_Maximum_Entropy_Model_for_Learning_Text_Chunking\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTowards Large Margin Speech Recognizers by Boosting and Discriminative Training (ICML 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCarsten Meyer, Peter Beyerlein\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.semanticscholar.org/paper/Towards-Large-Margin-Speech-Recognizers-by-Boosting-Meyer-Beyerlein/8408479e36da812cdbf6bc15f7849c3e76a1016d\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eIncorporating Prior Knowledge into Boosting (ICML 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRobert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra K. Gupta\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://rob.schapire.net/papers/boostknowledge.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eModeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation (ICML 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRobert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cs.utexas.edu/~ai-lab/pubs/ICML02-tac.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMARK: A Boosting Algorithm for Heterogeneous Kernel Models (KDD 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKristin P. Bennett, Michinari Momma, Mark J. Embrechts\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://homepages.rpiscrews.us/~bennek/papers/kdd2.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePredicting rare classes: can boosting make any weak learner strong (KDD 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMahesh V. Joshi, Ramesh C. Agarwal, Vipin Kumar\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.13.1159\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKernel Design Using Boosting (NIPS 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKoby Crammer, Joseph Keshet, Yoram Singer\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/ff79/344807e972fdd7e5e1c3ed5c539dd1aeecbe.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFloatBoost Learning for Classification (NIPS 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eStan Z. Li, ZhenQiu Zhang, Heung-Yeung Shum, HongJiang Zhang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/8ccc/5ef87eab96a4cae226750eba8322b30606ea.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDiscriminative Learning for Label Sequences via Boosting (NIPS 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYasemin Altun, Thomas Hofmann, Mark Johnson\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://web.science.mq.edu.au/~mjohnson/papers/nips02.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Density Estimation (NIPS 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSaharon Rosset, Eran Segal\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/2298-boosting-density-estimation.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSelf Supervised Boosting (NIPS 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMax Welling, Richard S. Zemel, Geoffrey E. Hinton\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/6a2a/f112a803e70c23b7055de2e73007cf42c301.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Dyadic Kernel Discriminants (NIPS 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBaback Moghaddam, Gregory Shakhnarovich\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.merl.com/publications/docs/TR2002-55.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Method to Boost Support Vector Machines (PAKDD 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLili Diao, Keyun Hu, Yuchang Lu, Chunyi Shi\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://elkingarcia.github.io/Papers/MLDM07.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Method to Boost Naive Bayesian Classifiers (PAKDD 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLili Diao, Keyun Hu, Yuchang Lu, Chunyi Shi\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/3-540-47887-6_11\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePredicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting (PKDD 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMahesh V. Joshi, Ramesh C. Agarwal, Vipin Kumar\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/3-540-45681-3_20\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eIterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance (PKDD 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYuta Choki, Einoshin Suzuki\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/3-540-45681-3_8\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eStaged Mixture Modelling and Boosting (UAI 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChristopher Meek, Bo Thiesson, David Heckerman\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1301.0586\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdvances in Boosting (UAI 2002)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRobert E. Schapire\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://rob.schapire.net/papers/uai02.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2001\u003c/h2\u003e\u003ca id=\"user-content-2001\" class=\"anchor\" aria-label=\"Permalink: 2001\" href=\"#2001\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eIs Regularization Unnecessary for Boosting? (AISTATS 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWenxin Jiang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/2439718_Is_Regularization_Unnecessary_for_Boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOnline Bagging and Boosting (AISTATS 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNikunj C. Oza, Stuart J. Russell\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ti.arc.nasa.gov/m/profile/oza/files/ozru01a.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eText Categorization Using Transductive Boosting (ECML 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHirotoshi Taira, Masahiko Haruno\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/3-540-44795-4_39\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eImproving Term Extraction by System Combination Using Boosting (ECML 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJordi Vivaldi, Lluís Màrquez, Horacio Rodríguez\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=3108351\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAnalysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example (ECML 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGünther Eibl, Karl Peter Pfeiffer\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/3-540-44795-4_10\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn the Practice of Branching Program Boosting (ECML 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTapio Elomaa, Matti Kääriäinen\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/221112522_On_the_Practice_of_Branching_Program_Boosting\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Mixture Models for Semi-supervised Learning (ICANN 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYves Grandvalet, Florence d'Alché-Buc, Christophe Ambroise\u003c/li\u003e\n\u003cli\u003e[[Paper]](\u003ca href=\"https://link.springer.com/chapter/10.1007/3-540-44668-0_7\" rel=\"nofollow\"\u003ehttps://link.springer.com/chapter/10.1007/3-540-44668-0_7\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods (ICDM 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBernard Zenko, Ljupco Todorovski, Saso Dzeroski\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.23.3118\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUsing Boosting to Simplify Classification Models (ICDM 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eVirginia Wheway\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/abstract/document/989565\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEvaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements (ICDM 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMahesh V. Joshi, Vipin Kumar, Ramesh C. Agarwal\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/b829/fe743e4beeeed65d32d2d7931354df7a2f60.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master\"\u003e[Code]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Neighborhood-Based Classifiers (ICML 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMarc Sebban, Richard Nock, Stéphane Lallich\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.semanticscholar.org/paper/Boosting-Neighborhood-Based-Classifiers-Sebban-Nock/ee88e3bbe8a7e81cae7ee53da2c824de7c82f882\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Noisy Data (ICML 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAbba Krieger, Chuan Long, Abraham J. Wyner\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/profile/Abba_Krieger/publication/221345435_Boosting_Noisy_Data/links/00463528a1ba641692000000.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSome Theoretical Aspects of Boosting in the Presence of Noisy Data (ICML 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWenxin Jiang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=2494A2C06ACA22FA971AC1C29B53FF62?doi=10.1.1.27.7231\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFilters, Wrappers and a Boosting-Based Hybrid for Feature Selection (ICML 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSanmay Das\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/93b6/25a0e35b59fa6a3e7dc1cbdb31268d62d69f.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eThe Distributed Boosting Algorithm (KDD 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAleksandar Lazarevic, Zoran Obradovic\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/2488971_The_Distributed_Boosting_Algorithm\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eExperimental Comparisons of Online and Batch Versions of Bagging and Boosting (KDD 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNikunj C. Oza, Stuart J. Russell\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://people.eecs.berkeley.edu/~russell/papers/kdd01-online.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSemi-supervised MarginBoost (NIPS 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFlorence d'Alché-Buc, Yves Grandvalet, Christophe Ambroise\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/2197/f1c2d55827b6928cc80030922569acce2d6c.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting and Maximum Likelihood for Exponential Models (NIPS 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGuy Lebanon, John D. Lafferty\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/2042-boosting-and-maximum-likelihood-for-exponential-models.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade (NIPS 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePaul A. Viola, Michael J. Jones\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.68.4306\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Localized Classifiers in Heterogeneous Databases (SDM 2001)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAleksandar Lazarevic, Zoran Obradovic\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://epubs.siam.org/doi/abs/10.1137/1.9781611972719.14\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2000\u003c/h2\u003e\u003ca id=\"user-content-2000\" class=\"anchor\" aria-label=\"Permalink: 2000\" href=\"#2000\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosted Wrapper Induction (AAAI 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDayne Freitag, Nicholas Kushmerick\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/d009/a2bd48a9d1971fbc0d99f6df00539a62048a.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAn Improved Boosting Algorithm and its Application to Text Categorization (CIKM 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFabrizio Sebastiani, Alessandro Sperduti, Nicola Valdambrini\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://nmis.isti.cnr.it/sebastiani/Publications/CIKM00.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting for Document Routing (CIKM 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRaj D. Iyer, David D. Lewis, Robert E. Schapire, Yoram Singer, Amit Singhal\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://singhal.info/cikm-2000.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn the Boosting Pruning Problem (ECML 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChristino Tamon, Jie Xiang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/3-540-45164-1_41\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Applied to Word Sense Disambiguation (ECML 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGerard Escudero, Lluís Màrquez, German Rigau\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=649539\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAn Empirical Study of MetaCost Using Boosting Algorithms (ECML 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKai Ming Ting\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.218.1624\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness (ICML 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJoseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/221345746_FeatureBoost_A_Meta-Learning_Algorithm_that_Improves_Model_Robustness\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eComparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of Discourse (ICML 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTadashi Nomoto, Yuji Matsumoto\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/221344998_Comparing_the_Minimum_Description_Length_Principle_and_Boosting_in_the_Automatic_Analysis_of_Discourse\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Boosting Approach to Topic Spotting on Subdialogues (ICML 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKary Myers, Michael J. Kearns, Satinder P. Singh, Marilyn A. Walker\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cis.upenn.edu/~mkearns/papers/topicspot.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Comparative Study of Cost-Sensitive Boosting Algorithms (ICML 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKai Ming Ting\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=657944\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting a Positive-Data-Only Learner (ICML 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAndrew R. Mitchell\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.3669\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Column Generation Algorithm For Boosting (ICML 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKristin P. Bennett, Ayhan Demiriz, John Shawe-Taylor\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=1828D5853F656BD6892E9C2C446ECC68?doi=10.1.1.16.9612\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Gradient-Based Boosting Algorithm for Regression Problems (NIPS 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRichard S. Zemel, Toniann Pitassi\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/c41a/9417f5605b55bdd216d119e47669a92f5c50.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eWeak Learners and Improved Rates of Convergence in Boosting (NIPS 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eShie Mannor, Ron Meir\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/1906-weak-learners-and-improved-rates-of-convergence-in-boosting.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdaptive Boosting for Spatial Functions with Unstable Driving Attributes (PAKDD 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAleksandar Lazarevic, Tim Fiez, Zoran Obradovic\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.dabi.temple.edu/~zoran/papers/lazarevic01j.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eScaling Up a Boosting-Based Learner via Adaptive Sampling (PAKDD 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCarlos Domingo, Osamu Watanabe\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/3-540-45571-X_37\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLearning First Order Logic Time Series Classifiers: Rules and Boosting (PKDD 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJuan J. Rodríguez Diez, Carlos Alonso González, Henrik Boström\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://people.dsv.su.se/~henke/papers/rodriguez00b.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBagging and Boosting with Dynamic Integration of Classifiers (PKDD 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAlexey Tsymbal, Seppo Puuronen\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://link.springer.com/chapter/10.1007/3-540-45372-5_12\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eText Filtering by Boosting Naive Bayes Classifiers (SIGIR 2000)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYu-Hwan Kim, Shang-Yoon Hahn, Byoung-Tak Zhang\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.researchgate.net/publication/221299823_Text_filtering_by_boosting_Naive_Bayes_classifiers\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1999\u003c/h2\u003e\u003ca id=\"user-content-1999\" class=\"anchor\" aria-label=\"Permalink: 1999\" href=\"#1999\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Methodology for Regression Problems (AISTATS 1999)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGreg Ridgeway, David Madigan, Thomas Richardson\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/5f19/6a8baa281b2190c4519305bec8f5c91c8e5a.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Applied to Tagging and PP Attachment (EMNLP 1999)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSteven Abney, Robert E. Schapire, Yoram Singer\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.aclweb.org/anthology/W99-0606\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ICML 1999)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eZijian Zheng, Geoffrey I. Webb, Kai Ming Ting\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/067e/86836ddbcb5e2844e955c16e058366a18c77.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdaCost: Misclassification Cost-Sensitive Boosting (ICML 1999)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip K. Chan\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/9ddf/bc2cc5c1b13b80a1a487b9caa57e80edd863.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting a Strong Learner: Evidence Against the Minimum Margin (ICML 1999)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMichael Bonnell Harries\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.acm.org/citation.cfm?id=657480\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting Algorithms as Gradient Descent (NIPS 1999)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLlew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/1766-boosting-algorithms-as-gradient-descent.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting with Multi-Way Branching in Decision Trees (NIPS 1999)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYishay Mansour, David A. McAllester\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/1659-boosting-with-multi-way-branching-in-decision-trees.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePotential Boosters (NIPS 1999)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNigel Duffy, David P. Helmbold\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/4884/c765b6ceab7bdfb6703489810c8a386fd2a8.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1998\u003c/h2\u003e\u003ca id=\"user-content-1998\" class=\"anchor\" aria-label=\"Permalink: 1998\" href=\"#1998\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAn Efficient Boosting Algorithm for Combining Preferences (ICML 1998)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://jmlr.csail.mit.edu/papers/volume4/freund03a/freund03a.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eQuery Learning Strategies Using Boosting and Bagging (ICML 1998)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNaoki Abe, Hiroshi Mamitsuka\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.bic.kyoto-u.ac.jp/pathway/mami/pubs/Files/icml98.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRegularizing AdaBoost (NIPS 1998)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGunnar Rätsch, Takashi Onoda, Klaus-Robert Müller\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/0afc/9de245547c675d40ad29240e2788c0416f91.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1997\u003c/h2\u003e\u003ca id=\"user-content-1997\" class=\"anchor\" aria-label=\"Permalink: 1997\" href=\"#1997\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBoosting the Margin: A New Explanation for the Effectiveness of Voting Methods (ICML 1997)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRobert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cc.gatech.edu/~isbell/tutorials/boostingmargins.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUsing Output Codes to Boost Multiclass Learning Problems (ICML 1997)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRobert E. Schapire\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://rob.schapire.net/papers/Schapire97.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eImproving Regressors Using Boosting Techniques (ICML 1997)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHarris Drucker\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/8d49/e2dedb817f2c3330e74b63c5fc86d2399ce3.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePruning Adaptive Boosting (ICML 1997)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDragos D. Margineantu, Thomas G. Dietterich\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pdfs.semanticscholar.org/b25f/615fc139fbdeccc3bcf4462f908d7f8e37f9.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTraining Methods for Adaptive Boosting of Neural Networks (NIPS 1997)\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHolger Schwenk, Yoshua Bengio\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/1335-training-methods-for-adaptive-boosting-of-neural-networks.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1996\u003c/h2\u003e\u003ca id=\"user-content-1996\" class=\"anchor\" aria-label=\"Permalink: 1996\" href=\"#1996\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eExperiments with a New Boosting Algorithm (ICML 1996)\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYoav Freund, Robert E. Schapire\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cseweb.ucsd.edu/~yfreund/papers/boostingexperiments.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1995\u003c/h2\u003e\u003ca id=\"user-content-1995\" class=\"anchor\" aria-label=\"Permalink: 1995\" href=\"#1995\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eBoosting Decision Trees (NIPS 1995)\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHarris Drucker, Corinna Cortes\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/1059-boosting-decision-trees.pdf\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1994\u003c/h2\u003e\u003ca id=\"user-content-1994\" class=\"anchor\" aria-label=\"Permalink: 1994\" href=\"#1994\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eBoosting and Other Machine Learning Algorithms (ICML 1994)\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHarris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sciencedirect.com/science/article/pii/B9781558603356500155\" rel=\"nofollow\"\u003e[Paper]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLicense\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/LICENSE\"\u003eCC0 Universal\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Awesome Gradient Boosting Research Papers.","anchor":"awesome-gradient-boosting-research-papers","htmlText":"Awesome Gradient Boosting Research Papers."},{"level":2,"text":"2023","anchor":"2023","htmlText":"2023"},{"level":2,"text":"2022","anchor":"2022","htmlText":"2022"},{"level":2,"text":"2021","anchor":"2021","htmlText":"2021"},{"level":2,"text":"2020","anchor":"2020","htmlText":"2020"},{"level":2,"text":"2019","anchor":"2019","htmlText":"2019"},{"level":2,"text":"2018","anchor":"2018","htmlText":"2018"},{"level":2,"text":"2017","anchor":"2017","htmlText":"2017"},{"level":2,"text":"2016","anchor":"2016","htmlText":"2016"},{"level":2,"text":"2015","anchor":"2015","htmlText":"2015"},{"level":2,"text":"2014","anchor":"2014","htmlText":"2014"},{"level":2,"text":"2013","anchor":"2013","htmlText":"2013"},{"level":2,"text":"2012","anchor":"2012","htmlText":"2012"},{"level":2,"text":"2011","anchor":"2011","htmlText":"2011"},{"level":2,"text":"2010","anchor":"2010","htmlText":"2010"},{"level":2,"text":"2009","anchor":"2009","htmlText":"2009"},{"level":2,"text":"2008","anchor":"2008","htmlText":"2008"},{"level":2,"text":"2007","anchor":"2007","htmlText":"2007"},{"level":2,"text":"2006","anchor":"2006","htmlText":"2006"},{"level":2,"text":"2005","anchor":"2005","htmlText":"2005"},{"level":2,"text":"2004","anchor":"2004","htmlText":"2004"},{"level":2,"text":"2003","anchor":"2003","htmlText":"2003"},{"level":2,"text":"2002","anchor":"2002","htmlText":"2002"},{"level":2,"text":"2001","anchor":"2001","htmlText":"2001"},{"level":2,"text":"2000","anchor":"2000","htmlText":"2000"},{"level":2,"text":"1999","anchor":"1999","htmlText":"1999"},{"level":2,"text":"1998","anchor":"1998","htmlText":"1998"},{"level":2,"text":"1997","anchor":"1997","htmlText":"1997"},{"level":2,"text":"1996","anchor":"1996","htmlText":"1996"},{"level":2,"text":"1995","anchor":"1995","htmlText":"1995"},{"level":2,"text":"1994","anchor":"1994","htmlText":"1994"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fbenedekrozemberczki%2Fawesome-gradient-boosting-papers"}},{"displayName":"code-of-conduct.md","repoName":"awesome-gradient-boosting-papers","refName":"master","path":"code-of-conduct.md","preferredFileType":"code_of_conduct","tabName":"Code of conduct","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fbenedekrozemberczki%2Fawesome-gradient-boosting-papers"}},{"displayName":"LICENSE","repoName":"awesome-gradient-boosting-papers","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"CC0-1.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fbenedekrozemberczki%2Fawesome-gradient-boosting-papers"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-96e76d5fdb2c.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> <div data-target="react-partial.reactRoot"><style data-styled="true" data-styled-version="5.3.11">.iVEunk{margin-top:16px;margin-bottom:16px;}/*!sc*/ .jzuOtQ{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:column;-ms-flex-direction:column;flex-direction:column;-webkit-box-pack:justify;-webkit-justify-content:space-between;-ms-flex-pack:justify;justify-content:space-between;}/*!sc*/ .bGojzy{margin-bottom:0;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:column;-ms-flex-direction:column;flex-direction:column;row-gap:16px;}/*!sc*/ .iNSVHo{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-box-pack:justify;-webkit-justify-content:space-between;-ms-flex-pack:justify;justify-content:space-between;-webkit-box-flex:1;-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1;padding-bottom:16px;padding-top:8px;}/*!sc*/ .bVgnfw{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;gap:8px;}/*!sc*/ @media screen and (max-width:320px){.bVgnfw{-webkit-box-flex:1;-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1;}}/*!sc*/ .CEgMp{position:relative;}/*!sc*/ @media screen and (max-width:380px){.CEgMp .ref-selector-button-text-container{max-width:80px;}}/*!sc*/ @media screen and (max-width:320px){.CEgMp{-webkit-box-flex:1;-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1;}.CEgMp .overview-ref-selector{width:100%;}.CEgMp .overview-ref-selector > span{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-box-pack:start;-webkit-justify-content:flex-start;-ms-flex-pack:start;justify-content:flex-start;}.CEgMp .overview-ref-selector > span > span[data-component="text"]{-webkit-box-flex:1;-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1;}}/*!sc*/ .gMOVLe[data-size="medium"]{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;min-width:0;}/*!sc*/ .gMOVLe[data-size="medium"] svg{color:var(--fgColor-muted,var(--color-fg-muted,#656d76));}/*!sc*/ .gMOVLe[data-size="medium"] > span{width:inherit;}/*!sc*/ .gUkoLg{-webkit-box-pack:center;-webkit-justify-content:center;-ms-flex-pack:center;justify-content:center;}/*!sc*/ .bZBlpz{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;width:100%;}/*!sc*/ .lhTYNA{margin-right:4px;color:var(--fgColor-muted,var(--color-fg-muted,#656d76));}/*!sc*/ .ffLUq{font-size:14px;min-width:0;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;}/*!sc*/ .bmcJak{min-width:0;}/*!sc*/ .fLXEGX{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;}/*!sc*/ @media screen and (max-width:1079px){.fLXEGX{display:none;}}/*!sc*/ .lmSMZJ[data-size="medium"]{color:var(--fgColor-muted,var(--color-fg-muted,#656d76));padding-left:4px;padding-right:4px;}/*!sc*/ .lmSMZJ[data-size="medium"] span[data-component="leadingVisual"]{margin-right:4px !important;}/*!sc*/ .dqfxud{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;}/*!sc*/ @media screen and (min-width:1080px){.dqfxud{display:none;}}/*!sc*/ @media screen and (max-width:543px){.dqfxud{display:none;}}/*!sc*/ .fGwBZA[data-size="medium"][data-no-visuals]{color:var(--fgColor-muted,var(--color-fg-muted,#656d76));}/*!sc*/ .jxTzTd{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;padding-left:8px;gap:8px;}/*!sc*/ .gqqBXN{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;gap:8px;}/*!sc*/ @media screen and (max-width:543px){.gqqBXN{display:none;}}/*!sc*/ .dzXgxt{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;}/*!sc*/ @media screen and (max-width:1011px){.dzXgxt{display:none;}}/*!sc*/ .iWFGlI{margin-left:8px;margin-right:8px;margin:0;}/*!sc*/ .vcvyP{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;min-width:160px;}/*!sc*/ .YUPas{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;}/*!sc*/ @media screen and (min-width:1012px){.YUPas{display:none;}}/*!sc*/ .izFOf{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;}/*!sc*/ @media screen and (min-width:544px){.izFOf{display:none;}}/*!sc*/ .vIPPs{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:column;-ms-flex-direction:column;flex-direction:column;gap:16px;}/*!sc*/ .fdROMU{width:100%;border-collapse:separate;border-spacing:0;border:1px solid;border-color:var(--borderColor-default,var(--color-border-default,#d0d7de));border-radius:6px;table-layout:fixed;overflow:unset;}/*!sc*/ .jGKpsv{height:0px;line-height:0px;}/*!sc*/ .jGKpsv tr{height:0px;font-size:0px;}/*!sc*/ .jdgHnn{padding:16px;color:var(--fgColor-muted,var(--color-fg-muted,#656d76));font-size:12px;text-align:left;height:40px;}/*!sc*/ .jdgHnn th{padding-left:16px;background-color:var(--bgColor-muted,var(--color-canvas-subtle,#f6f8fa));}/*!sc*/ .bQivRW{width:100%;border-top-left-radius:6px;}/*!sc*/ @media screen and (min-width:544px){.bQivRW{display:none;}}/*!sc*/ .ldkMIO{width:40%;border-top-left-radius:6px;}/*!sc*/ @media screen and (max-width:543px){.ldkMIO{display:none;}}/*!sc*/ .jMbWeI{text-align:right;padding-right:16px;width:136px;border-top-right-radius:6px;}/*!sc*/ .gpqjiB{color:var(--fgColor-muted,var(--color-fg-muted,#656d76));font-size:12px;height:40px;}/*!sc*/ .dzCJzi{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:justify;-webkit-justify-content:space-between;-ms-flex-pack:justify;justify-content:space-between;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;gap:8px;min-width:273px;padding:8px;}/*!sc*/ @media screen and (min-width:544px){.dzCJzi{-webkit-flex-wrap:nowrap;-ms-flex-wrap:nowrap;flex-wrap:nowrap;}}/*!sc*/ .eNCcrz{text-align:center;vertical-align:center;height:40px;border-top:1px solid;border-color:var(--borderColor-default,var(--color-border-default,#d0d7de));}/*!sc*/ .bHTcCe{border-top:1px solid var(--borderColor-default,var(--color-border-default));cursor:pointer;}/*!sc*/ .csrIcr{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-box-flex:1;-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1;gap:16px;}/*!sc*/ .bUQNHB{border:1px solid;border-color:var(--borderColor-default,var(--color-border-default,#d0d7de));border-radius:6px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:column;-ms-flex-direction:column;flex-direction:column;-webkit-box-flex:1;-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1;}/*!sc*/ @media screen and (max-width:543px){.bUQNHB{margin-left:-16px;margin-right:-16px;max-width:calc(100% + 32px);}}/*!sc*/ @media screen and (min-width:544px){.bUQNHB{max-width:100%;}}/*!sc*/ .jPdcfu{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;border-bottom:1px solid;border-bottom-color:var(--borderColor-default,var(--color-border-default,#d0d7de));-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;padding-right:8px;position:-webkit-sticky;position:sticky;top:0;background-color:var(--bgColor-default,var(--color-canvas-default,#ffffff));z-index:1;border-top-left-radius:6px;border-top-right-radius:6px;}/*!sc*/ .iphEWz{-webkit-box-flex:1;-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1;border-bottom:none;max-width:100%;padding-left:8px;padding-right:8px;}/*!sc*/ .hUCRAk{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:column;-ms-flex-direction:column;flex-direction:column;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}/*!sc*/ .cwoBXV[data-size="medium"]{color:var(--fgColor-muted,var(--color-fg-subtle,#6e7781));padding-left:8px;padding-right:8px;}/*!sc*/ .QkQOb{padding:32px;overflow:auto;}/*!sc*/ data-styled.g1[id="Box-sc-g0xbh4-0"]{content:"iVEunk,jzuOtQ,bGojzy,iNSVHo,bVgnfw,CEgMp,gMOVLe,gUkoLg,bZBlpz,lhTYNA,ffLUq,bmcJak,fLXEGX,lmSMZJ,dqfxud,fGwBZA,jxTzTd,gqqBXN,dzXgxt,iWFGlI,vcvyP,YUPas,izFOf,vIPPs,fdROMU,jGKpsv,jdgHnn,bQivRW,ldkMIO,jMbWeI,gpqjiB,dzCJzi,eNCcrz,bHTcCe,csrIcr,bUQNHB,jPdcfu,iphEWz,hUCRAk,cwoBXV,QkQOb,"}/*!sc*/ .brGdpi{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;-webkit-clip:rect(0,0,0,0);clip:rect(0,0,0,0);white-space:nowrap;border-width:0;}/*!sc*/ data-styled.g5[id="_VisuallyHidden__VisuallyHidden-sc-11jhm7a-0"]{content:"brGdpi,"}/*!sc*/ .hWlpPn{position:relative;display:inline-block;}/*!sc*/ .hWlpPn::after{position:absolute;z-index:1000000;display:none;padding:0.5em 0.75em;font:normal normal 11px/1.5 -apple-system,BlinkMacSystemFont,"Segoe UI","Noto Sans",Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji";-webkit-font-smoothing:subpixel-antialiased;color:var(--tooltip-fgColor,var(--fgColor-onEmphasis,var(--color-fg-on-emphasis,#ffffff)));text-align:center;-webkit-text-decoration:none;text-decoration:none;text-shadow:none;text-transform:none;-webkit-letter-spacing:normal;-moz-letter-spacing:normal;-ms-letter-spacing:normal;letter-spacing:normal;word-wrap:break-word;white-space:pre;pointer-events:none;content:attr(aria-label);background:var(--tooltip-bgColor,var(--bgColor-emphasis,var(--color-neutral-emphasis-plus,#24292f)));border-radius:6px;opacity:0;}/*!sc*/ @-webkit-keyframes tooltip-appear{from{opacity:0;}to{opacity:1;}}/*!sc*/ @keyframes tooltip-appear{from{opacity:0;}to{opacity:1;}}/*!sc*/ .hWlpPn:hover::after,.hWlpPn:active::after,.hWlpPn:focus::after,.hWlpPn:focus-within::after{display:inline-block;-webkit-text-decoration:none;text-decoration:none;-webkit-animation-name:tooltip-appear;animation-name:tooltip-appear;-webkit-animation-duration:0.1s;animation-duration:0.1s;-webkit-animation-fill-mode:forwards;animation-fill-mode:forwards;-webkit-animation-timing-function:ease-in;animation-timing-function:ease-in;-webkit-animation-delay:0s;animation-delay:0s;}/*!sc*/ .hWlpPn.tooltipped-no-delay:hover::after,.hWlpPn.tooltipped-no-delay:active::after,.hWlpPn.tooltipped-no-delay:focus::after,.hWlpPn.tooltipped-no-delay:focus-within::after{-webkit-animation-delay:0s;animation-delay:0s;}/*!sc*/ .hWlpPn.tooltipped-multiline:hover::after,.hWlpPn.tooltipped-multiline:active::after,.hWlpPn.tooltipped-multiline:focus::after,.hWlpPn.tooltipped-multiline:focus-within::after{display:table-cell;}/*!sc*/ .hWlpPn.tooltipped-s::after,.hWlpPn.tooltipped-se::after,.hWlpPn.tooltipped-sw::after{top:100%;right:50%;margin-top:6px;}/*!sc*/ .hWlpPn.tooltipped-se::after{right:auto;left:50%;margin-left:-16px;}/*!sc*/ .hWlpPn.tooltipped-sw::after{margin-right:-16px;}/*!sc*/ .hWlpPn.tooltipped-n::after,.hWlpPn.tooltipped-ne::after,.hWlpPn.tooltipped-nw::after{right:50%;bottom:100%;margin-bottom:6px;}/*!sc*/ .hWlpPn.tooltipped-ne::after{right:auto;left:50%;margin-left:-16px;}/*!sc*/ .hWlpPn.tooltipped-nw::after{margin-right:-16px;}/*!sc*/ .hWlpPn.tooltipped-s::after,.hWlpPn.tooltipped-n::after{-webkit-transform:translateX(50%);-ms-transform:translateX(50%);transform:translateX(50%);}/*!sc*/ .hWlpPn.tooltipped-w::after{right:100%;bottom:50%;margin-right:6px;-webkit-transform:translateY(50%);-ms-transform:translateY(50%);transform:translateY(50%);}/*!sc*/ .hWlpPn.tooltipped-e::after{bottom:50%;left:100%;margin-left:6px;-webkit-transform:translateY(50%);-ms-transform:translateY(50%);transform:translateY(50%);}/*!sc*/ .hWlpPn.tooltipped-multiline::after{width:-webkit-max-content;width:-moz-max-content;width:max-content;max-width:250px;word-wrap:break-word;white-space:pre-line;border-collapse:separate;}/*!sc*/ .hWlpPn.tooltipped-multiline.tooltipped-s::after,.hWlpPn.tooltipped-multiline.tooltipped-n::after{right:auto;left:50%;-webkit-transform:translateX(-50%);-ms-transform:translateX(-50%);transform:translateX(-50%);}/*!sc*/ .hWlpPn.tooltipped-multiline.tooltipped-w::after,.hWlpPn.tooltipped-multiline.tooltipped-e::after{right:100%;}/*!sc*/ .hWlpPn.tooltipped-align-right-2::after{right:0;margin-right:0;}/*!sc*/ .hWlpPn.tooltipped-align-left-2::after{left:0;margin-left:0;}/*!sc*/ data-styled.g16[id="Tooltip__TooltipBase-sc-17tf59c-0"]{content:"hWlpPn,"}/*!sc*/ .liVpTx{display:inline-block;overflow:hidden;text-overflow:ellipsis;vertical-align:top;white-space:nowrap;max-width:125px;}/*!sc*/ data-styled.g18[id="Truncate__StyledTruncate-sc-23o1d2-0"]{content:"liVpTx,"}/*!sc*/ </style> <!-- --> <!-- --> <div class="Box-sc-g0xbh4-0 iVEunk"><div class="Box-sc-g0xbh4-0 jzuOtQ"><div class="Box-sc-g0xbh4-0 bGojzy"></div></div><div class="Box-sc-g0xbh4-0 iNSVHo"><div class="Box-sc-g0xbh4-0 bVgnfw"><div class="Box-sc-g0xbh4-0 CEgMp"><button type="button" aria-haspopup="true" aria-expanded="false" tabindex="0" aria-label="master branch" data-testid="anchor-button" class="Box-sc-g0xbh4-0 gMOVLe prc-Button-ButtonBase-c50BI overview-ref-selector width-full" data-loading="false" data-size="medium" data-variant="default" aria-describedby="branch-picker-repos-header-ref-selector-loading-announcement" id="branch-picker-repos-header-ref-selector"><span data-component="buttonContent" class="Box-sc-g0xbh4-0 gUkoLg prc-Button-ButtonContent-HKbr-"><span data-component="text" class="prc-Button-Label-pTQ3x"><div class="Box-sc-g0xbh4-0 bZBlpz"><div class="Box-sc-g0xbh4-0 lhTYNA"><svg aria-hidden="true" focusable="false" class="octicon octicon-git-branch" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M9.5 3.25a2.25 2.25 0 1 1 3 2.122V6A2.5 2.5 0 0 1 10 8.5H6a1 1 0 0 0-1 1v1.128a2.251 2.251 0 1 1-1.5 0V5.372a2.25 2.25 0 1 1 1.5 0v1.836A2.493 2.493 0 0 1 6 7h4a1 1 0 0 0 1-1v-.628A2.25 2.25 0 0 1 9.5 3.25Zm-6 0a.75.75 0 1 0 1.5 0 .75.75 0 0 0-1.5 0Zm8.25-.75a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5ZM4.25 12a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5Z"></path></svg></div><div class="Box-sc-g0xbh4-0 ffLUq ref-selector-button-text-container"><span class="Box-sc-g0xbh4-0 bmcJak prc-Text-Text-0ima0"> <!-- -->master</span></div></div></span><span data-component="trailingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-triangle-down" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m4.427 7.427 3.396 3.396a.25.25 0 0 0 .354 0l3.396-3.396A.25.25 0 0 0 11.396 7H4.604a.25.25 0 0 0-.177.427Z"></path></svg></span></span></button><button hidden="" data-hotkey-scope="read-only-cursor-text-area"></button></div><div class="Box-sc-g0xbh4-0 fLXEGX"><a style="--button-color:fg.muted" type="button" href="/benedekrozemberczki/awesome-gradient-boosting-papers/branches" class="Box-sc-g0xbh4-0 lmSMZJ prc-Button-ButtonBase-c50BI" data-loading="false" data-size="medium" data-variant="invisible" aria-describedby=":Rclab:-loading-announcement"><span data-component="buttonContent" class="Box-sc-g0xbh4-0 gUkoLg prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-git-branch" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M9.5 3.25a2.25 2.25 0 1 1 3 2.122V6A2.5 2.5 0 0 1 10 8.5H6a1 1 0 0 0-1 1v1.128a2.251 2.251 0 1 1-1.5 0V5.372a2.25 2.25 0 1 1 1.5 0v1.836A2.493 2.493 0 0 1 6 7h4a1 1 0 0 0 1-1v-.628A2.25 2.25 0 0 1 9.5 3.25Zm-6 0a.75.75 0 1 0 1.5 0 .75.75 0 0 0-1.5 0Zm8.25-.75a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5ZM4.25 12a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x">Branches</span></span></a><a style="--button-color:fg.muted" type="button" href="/benedekrozemberczki/awesome-gradient-boosting-papers/tags" class="Box-sc-g0xbh4-0 lmSMZJ prc-Button-ButtonBase-c50BI" data-loading="false" data-size="medium" data-variant="invisible" aria-describedby=":Rklab:-loading-announcement"><span data-component="buttonContent" class="Box-sc-g0xbh4-0 gUkoLg prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-tag" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x">Tags</span></span></a></div><div class="Box-sc-g0xbh4-0 dqfxud"><a style="--button-color:fg.muted" type="button" aria-label="Go to Branches page" href="/benedekrozemberczki/awesome-gradient-boosting-papers/branches" class="Box-sc-g0xbh4-0 fGwBZA prc-Button-ButtonBase-c50BI" data-loading="false" data-no-visuals="true" data-size="medium" data-variant="invisible" aria-describedby=":Relab:-loading-announcement"><svg aria-hidden="true" focusable="false" class="octicon octicon-git-branch" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M9.5 3.25a2.25 2.25 0 1 1 3 2.122V6A2.5 2.5 0 0 1 10 8.5H6a1 1 0 0 0-1 1v1.128a2.251 2.251 0 1 1-1.5 0V5.372a2.25 2.25 0 1 1 1.5 0v1.836A2.493 2.493 0 0 1 6 7h4a1 1 0 0 0 1-1v-.628A2.25 2.25 0 0 1 9.5 3.25Zm-6 0a.75.75 0 1 0 1.5 0 .75.75 0 0 0-1.5 0Zm8.25-.75a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5ZM4.25 12a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5Z"></path></svg></a><a style="--button-color:fg.muted" type="button" aria-label="Go to Tags page" href="/benedekrozemberczki/awesome-gradient-boosting-papers/tags" class="Box-sc-g0xbh4-0 fGwBZA prc-Button-ButtonBase-c50BI" data-loading="false" data-no-visuals="true" data-size="medium" data-variant="invisible" aria-describedby=":Rmlab:-loading-announcement"><svg aria-hidden="true" focusable="false" class="octicon octicon-tag" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path></svg></a></div></div><div class="Box-sc-g0xbh4-0 jxTzTd"><div class="Box-sc-g0xbh4-0 gqqBXN"><div class="Box-sc-g0xbh4-0 dzXgxt"><!--$--><div class="Box-sc-g0xbh4-0 iWFGlI"><span class="Box-sc-g0xbh4-0 vcvyP TextInput-wrapper prc-components-TextInputWrapper-i1ofR prc-components-TextInputBaseWrapper-ueK9q" data-leading-visual="true" data-trailing-visual="true" aria-busy="false"><span class="TextInput-icon" id=":R2j5ab:" aria-hidden="true"><svg aria-hidden="true" focusable="false" class="octicon octicon-search" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M10.68 11.74a6 6 0 0 1-7.922-8.982 6 6 0 0 1 8.982 7.922l3.04 3.04a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215ZM11.5 7a4.499 4.499 0 1 0-8.997 0A4.499 4.499 0 0 0 11.5 7Z"></path></svg></span><input type="text" aria-label="Go to file" role="combobox" aria-controls="file-results-list" aria-expanded="false" aria-haspopup="dialog" autoCorrect="off" spellcheck="false" placeholder="Go to file" aria-describedby=":R2j5ab: :R2j5abH1:" data-component="input" class="prc-components-Input-Ic-y8" value=""/><span class="TextInput-icon" id=":R2j5abH1:" aria-hidden="true"></span></span></div><!--/$--></div><div class="Box-sc-g0xbh4-0 YUPas"><button type="button" class="prc-Button-ButtonBase-c50BI" data-loading="false" data-no-visuals="true" data-size="medium" data-variant="default" aria-describedby=":Rr5ab:-loading-announcement"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="text" class="prc-Button-Label-pTQ3x">Go to file</span></span></button></div><div class="react-directory-add-file-icon"></div><div class="react-directory-remove-file-icon"></div></div><button type="button" aria-haspopup="true" aria-expanded="false" tabindex="0" class="prc-Button-ButtonBase-c50BI" data-loading="false" data-size="medium" data-variant="primary" aria-describedby=":R55ab:-loading-announcement" id=":R55ab:"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-code hide-sm" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m11.28 3.22 4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734L13.94 8l-3.72-3.72a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215Zm-6.56 0a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042L2.06 8l3.72 3.72a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L.47 8.53a.75.75 0 0 1 0-1.06Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x">Code</span><span data-component="trailingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-triangle-down" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m4.427 7.427 3.396 3.396a.25.25 0 0 0 .354 0l3.396-3.396A.25.25 0 0 0 11.396 7H4.604a.25.25 0 0 0-.177.427Z"></path></svg></span></span></button><div class="Box-sc-g0xbh4-0 izFOf"><button data-component="IconButton" type="button" aria-label="Open more actions menu" aria-haspopup="true" aria-expanded="false" tabindex="0" class="prc-Button-ButtonBase-c50BI prc-Button-IconButton-szpyj" data-loading="false" data-no-visuals="true" data-size="medium" data-variant="default" aria-describedby=":R75ab:-loading-announcement" id=":R75ab:"><svg aria-hidden="true" focusable="false" class="octicon octicon-kebab-horizontal" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M8 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3ZM1.5 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Zm13 0a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path></svg></button></div></div></div><div class="Box-sc-g0xbh4-0 vIPPs"><div data-hpc="true"><button hidden="" data-testid="focus-next-element-button" data-hotkey="j"></button><button hidden="" data-testid="focus-previous-element-button" data-hotkey="k"></button><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading" id="folders-and-files">Folders and files</h2><table aria-labelledby="folders-and-files" class="Box-sc-g0xbh4-0 fdROMU"><thead class="Box-sc-g0xbh4-0 jGKpsv"><tr class="Box-sc-g0xbh4-0 jdgHnn"><th colSpan="2" class="Box-sc-g0xbh4-0 bQivRW"><span class="text-bold">Name</span></th><th colSpan="1" class="Box-sc-g0xbh4-0 ldkMIO"><span class="text-bold">Name</span></th><th class="hide-sm"><div title="Last commit message" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit message</span></div></th><th colSpan="1" class="Box-sc-g0xbh4-0 jMbWeI"><div title="Last commit date" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit date</span></div></th></tr></thead><tbody><tr class="Box-sc-g0xbh4-0 gpqjiB"><td colSpan="3" class="bgColor-muted p-1 rounded-top-2"><div class="Box-sc-g0xbh4-0 dzCJzi"><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading">Latest commit</h2><div style="width:120px" class="Skeleton Skeleton--text" data-testid="loading"> </div><div class="d-flex flex-shrink-0 gap-2"><div data-testid="latest-commit-details" class="d-none d-sm-flex flex-items-center"></div><div class="d-flex gap-2"><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading">History</h2><a href="/benedekrozemberczki/awesome-gradient-boosting-papers/commits/master/" class="prc-Button-ButtonBase-c50BI d-none d-lg-flex LinkButton-module__code-view-link-button--xvCGA flex-items-center fgColor-default" data-loading="false" data-size="small" data-variant="invisible" aria-describedby=":Raqj8pab:-loading-announcement"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x"><span class="fgColor-default">206 Commits</span></span></span></a><div class="d-sm-none"></div><div class="d-flex d-lg-none"><span role="tooltip" aria-label="206 Commits" id="history-icon-button-tooltip" class="Tooltip__TooltipBase-sc-17tf59c-0 hWlpPn tooltipped-n"><a href="/benedekrozemberczki/awesome-gradient-boosting-papers/commits/master/" class="prc-Button-ButtonBase-c50BI LinkButton-module__code-view-link-button--xvCGA flex-items-center fgColor-default" data-loading="false" data-size="small" data-variant="invisible" aria-describedby=":R1iqj8pab:-loading-announcement history-icon-button-tooltip"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path></svg></span></span></a></span></div></div></div></div></td></tr><tr class="react-directory-row undefined" id="folder-row-0"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title=".github" aria-label=".github, (Directory)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/tree/master/.github">.github</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title=".github" aria-label=".github, (Directory)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/tree/master/.github">.github</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-1"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="LICENSE" aria-label="LICENSE, (File)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/LICENSE">LICENSE</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="LICENSE" aria-label="LICENSE, (File)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/LICENSE">LICENSE</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-2"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="README.md" aria-label="README.md, (File)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/README.md">README.md</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="README.md" aria-label="README.md, (File)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/README.md">README.md</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-3"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="awesome.py" aria-label="awesome.py, (File)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/awesome.py">awesome.py</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="awesome.py" aria-label="awesome.py, (File)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/awesome.py">awesome.py</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-4"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="boosting.gif" aria-label="boosting.gif, (File)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/boosting.gif">boosting.gif</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="boosting.gif" aria-label="boosting.gif, (File)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/boosting.gif">boosting.gif</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-5"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="code-of-conduct.md" aria-label="code-of-conduct.md, (File)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/code-of-conduct.md">code-of-conduct.md</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="code-of-conduct.md" aria-label="code-of-conduct.md, (File)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/code-of-conduct.md">code-of-conduct.md</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-6"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="contributing.md" aria-label="contributing.md, (File)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/contributing.md">contributing.md</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="contributing.md" aria-label="contributing.md, (File)" class="Link--primary" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/contributing.md">contributing.md</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="Box-sc-g0xbh4-0 eNCcrz d-none" data-testid="view-all-files-row"><td colSpan="3" class="Box-sc-g0xbh4-0 bHTcCe"><div><button class="prc-Link-Link-85e08">View all files</button></div></td></tr></tbody></table></div><div class="Box-sc-g0xbh4-0 csrIcr"><div class="Box-sc-g0xbh4-0 bUQNHB"><div itemscope="" itemType="https://schema.org/abstract" class="Box-sc-g0xbh4-0 jPdcfu"><h2 class="_VisuallyHidden__VisuallyHidden-sc-11jhm7a-0 brGdpi">Repository files navigation</h2><nav class="Box-sc-g0xbh4-0 iphEWz prc-components-UnderlineWrapper-oOh5J" aria-label="Repository files"><ul class="prc-components-UnderlineItemList-b23Hf" role="list"><li class="Box-sc-g0xbh4-0 hUCRAk"><a class="prc-components-UnderlineItem-lJsg-" href="#" aria-current="page"><span data-component="icon"><svg aria-hidden="true" focusable="false" class="octicon octicon-book" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M0 1.75A.75.75 0 0 1 .75 1h4.253c1.227 0 2.317.59 3 1.501A3.743 3.743 0 0 1 11.006 1h4.245a.75.75 0 0 1 .75.75v10.5a.75.75 0 0 1-.75.75h-4.507a2.25 2.25 0 0 0-1.591.659l-.622.621a.75.75 0 0 1-1.06 0l-.622-.621A2.25 2.25 0 0 0 5.258 13H.75a.75.75 0 0 1-.75-.75Zm7.251 10.324.004-5.073-.002-2.253A2.25 2.25 0 0 0 5.003 2.5H1.5v9h3.757a3.75 3.75 0 0 1 1.994.574ZM8.755 4.75l-.004 7.322a3.752 3.752 0 0 1 1.992-.572H14.5v-9h-3.495a2.25 2.25 0 0 0-2.25 2.25Z"></path></svg></span><span data-component="text" data-content="README">README</span></a></li><li class="Box-sc-g0xbh4-0 hUCRAk"><a class="prc-components-UnderlineItem-lJsg-" href="#"><span data-component="icon"><svg aria-hidden="true" focusable="false" class="octicon octicon-code-of-conduct" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M8.048 2.241c.964-.709 2.079-1.238 3.325-1.241a4.616 4.616 0 0 1 3.282 1.355c.41.408.757.86.996 1.428.238.568.348 1.206.347 1.968 0 2.193-1.505 4.254-3.081 5.862-1.496 1.526-3.213 2.796-4.249 3.563l-.22.163a.749.749 0 0 1-.895 0l-.221-.163c-1.036-.767-2.753-2.037-4.249-3.563C1.51 10.008.007 7.952.002 5.762a4.614 4.614 0 0 1 1.353-3.407C3.123.585 6.223.537 8.048 2.24Zm-1.153.983c-1.25-1.033-3.321-.967-4.48.191a3.115 3.115 0 0 0-.913 2.335c0 1.556 1.109 3.24 2.652 4.813C5.463 11.898 6.96 13.032 8 13.805c.353-.262.758-.565 1.191-.905l-1.326-1.223a.75.75 0 0 1 1.018-1.102l1.48 1.366c.328-.281.659-.577.984-.887L9.99 9.802a.75.75 0 1 1 1.019-1.103l1.384 1.28c.295-.329.566-.661.81-.995L12.92 8.7l-1.167-1.168c-.674-.671-1.78-.664-2.474.03-.268.269-.538.537-.802.797-.893.882-2.319.843-3.185-.032-.346-.35-.693-.697-1.043-1.047a.75.75 0 0 1-.04-1.016c.162-.191.336-.401.52-.623.62-.748 1.356-1.637 2.166-2.417Zm7.112 4.442c.313-.65.491-1.293.491-1.916v-.001c0-.614-.088-1.045-.23-1.385-.143-.339-.357-.633-.673-.949a3.111 3.111 0 0 0-2.218-.915c-1.092.003-2.165.627-3.226 1.602-.823.755-1.554 1.637-2.228 2.45l-.127.154.562.566a.755.755 0 0 0 1.066.02l.794-.79c1.258-1.258 3.312-1.31 4.594-.032.396.394.792.791 1.173 1.173Z"></path></svg></span><span data-component="text" data-content="Code of conduct">Code of conduct</span></a></li><li class="Box-sc-g0xbh4-0 hUCRAk"><a class="prc-components-UnderlineItem-lJsg-" href="#"><span data-component="icon"><svg aria-hidden="true" focusable="false" class="octicon octicon-law" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M8.75.75V2h.985c.304 0 .603.08.867.231l1.29.736c.038.022.08.033.124.033h2.234a.75.75 0 0 1 0 1.5h-.427l2.111 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.006.005-.01.01-.045.04c-.21.176-.441.327-.686.45C14.556 10.78 13.88 11 13 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L12.178 4.5h-.162c-.305 0-.604-.079-.868-.231l-1.29-.736a.245.245 0 0 0-.124-.033H8.75V13h2.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1 0-1.5h2.5V3.5h-.984a.245.245 0 0 0-.124.033l-1.289.737c-.265.15-.564.23-.869.23h-.162l2.112 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.016.015-.045.04c-.21.176-.441.327-.686.45C4.556 10.78 3.88 11 3 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L2.178 4.5H1.75a.75.75 0 0 1 0-1.5h2.234a.249.249 0 0 0 .125-.033l1.288-.737c.265-.15.564-.23.869-.23h.984V.75a.75.75 0 0 1 1.5 0Zm2.945 8.477c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L13 6.327Zm-10 0c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L3 6.327Z"></path></svg></span><span data-component="text" data-content="CC0-1.0 license">CC0-1.0 license</span></a></li></ul></nav><button style="--button-color:fg.subtle" type="button" aria-label="Outline" aria-haspopup="true" aria-expanded="false" tabindex="0" class="Box-sc-g0xbh4-0 cwoBXV prc-Button-ButtonBase-c50BI" data-loading="false" data-size="medium" data-variant="invisible" aria-describedby=":Rr9ab:-loading-announcement" id=":Rr9ab:"><svg aria-hidden="true" focusable="false" class="octicon octicon-list-unordered" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Awesome Gradient Boosting Research Papers.</h1><a id="user-content-awesome-gradient-boosting-research-papers" class="anchor" aria-label="Permalink: Awesome Gradient Boosting Research Papers." href="#awesome-gradient-boosting-research-papers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://github.com/sindresorhus/awesome"><img src="https://camo.githubusercontent.com/8693bde04030b1670d5097703441005eba34240c32d1df1eb82a5f0d6716518e/68747470733a2f2f63646e2e7261776769742e636f6d2f73696e647265736f726875732f617765736f6d652f643733303566333864323966656437386661383536353265336136336531353464643865383832392f6d656469612f62616467652e737667" alt="Awesome" data-canonical-src="https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg" style="max-width: 100%;"></a> <a href="http://makeapullrequest.com" rel="nofollow"><img src="https://camo.githubusercontent.com/88482ebfc5e3e4f2d667148ab6a3eb55948789f1dba71dfa0eb2e05afe02958c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5052732d77656c636f6d652d627269676874677265656e2e7376673f7374796c653d666c61742d737175617265" alt="PRs Welcome" data-canonical-src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square" style="max-width: 100%;"></a> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/2982d26d63a27ecb81c1160e90d956bd83a6391e00002b3c6b2befb3a2214d48/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f62656e6564656b726f7a656d626572637a6b692f617765736f6d652d6772616469656e742d626f6f7374696e672d7061706572732e7376673f636f6c6f723d626c7565"><img src="https://camo.githubusercontent.com/2982d26d63a27ecb81c1160e90d956bd83a6391e00002b3c6b2befb3a2214d48/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f62656e6564656b726f7a656d626572637a6b692f617765736f6d652d6772616469656e742d626f6f7374696e672d7061706572732e7376673f636f6c6f723d626c7565" alt="License" data-canonical-src="https://img.shields.io/github/license/benedekrozemberczki/awesome-gradient-boosting-papers.svg?color=blue" style="max-width: 100%;"></a> <a href="https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers/archive/master.zip"><img src="https://camo.githubusercontent.com/8de93fc248cea4512d237cd88326b05ef11b3f22dae091ac8a180f68f23dcb0a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f7265706f2d73697a652f62656e6564656b726f7a656d626572637a6b692f617765736f6d652d6772616469656e742d626f6f7374696e672d7061706572732e737667" alt="repo size" data-canonical-src="https://img.shields.io/github/repo-size/benedekrozemberczki/awesome-gradient-boosting-papers.svg" style="max-width: 100%;"></a> <a href="https://twitter.com/intent/follow?screen_name=benrozemberczki" rel="nofollow"><img src="https://camo.githubusercontent.com/dcb0f569a415bbaea3fcda28bb9fb3f9159dea5706937e0046a7853072228c89/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f62656e726f7a656d626572637a6b693f7374796c653d736f6369616c266c6f676f3d74776974746572" alt="benedekrozemberczki" data-canonical-src="https://img.shields.io/twitter/follow/benrozemberczki?style=social&logo=twitter" style="max-width: 100%;"></a></p> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/boosting.gif"><img width="450" src="/benedekrozemberczki/awesome-gradient-boosting-papers/raw/master/boosting.gif" data-animated-image="" style="max-width: 100%;"></a> </p> <hr> <p dir="auto">A curated list of gradient and adaptive boosting papers with implementations from the following conferences:</p> <ul dir="auto"> <li>Machine learning <ul dir="auto"> <li><a href="https://nips.cc/" rel="nofollow">NeurIPS</a></li> <li><a href="https://icml.cc/" rel="nofollow">ICML</a></li> <li><a href="https://iclr.cc/" rel="nofollow">ICLR</a></li> </ul> </li> <li>Computer vision <ul dir="auto"> <li><a href="http://cvpr2019.thecvf.com/" rel="nofollow">CVPR</a></li> <li><a href="http://iccv2019.thecvf.com/" rel="nofollow">ICCV</a></li> <li><a href="https://eccv2018.org/" rel="nofollow">ECCV</a></li> </ul> </li> <li>Natural language processing <ul dir="auto"> <li><a href="http://www.acl2019.org/EN/index.xhtml" rel="nofollow">ACL</a></li> <li><a href="https://naacl2019.org/" rel="nofollow">NAACL</a></li> <li><a href="https://www.emnlp-ijcnlp2019.org/" rel="nofollow">EMNLP</a></li> </ul> </li> <li>Data <ul dir="auto"> <li><a href="https://www.kdd.org/" rel="nofollow">KDD</a></li> <li><a href="http://www.cikmconference.org/" rel="nofollow">CIKM</a></li> <li><a href="http://icdm2019.bigke.org/" rel="nofollow">ICDM</a></li> <li><a href="https://www.siam.org/Conferences/CM/Conference/sdm19" rel="nofollow">SDM</a></li> <li><a href="http://pakdd2019.medmeeting.org" rel="nofollow">PAKDD</a></li> <li><a href="http://ecmlpkdd2019.org" rel="nofollow">PKDD/ECML</a></li> <li><a href="https://recsys.acm.org/" rel="nofollow">RECSYS</a></li> <li><a href="https://sigir.org/" rel="nofollow">SIGIR</a></li> <li><a href="https://www2019.thewebconf.org/" rel="nofollow">WWW</a></li> <li><a href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/www.wsdm-conference.org">WSDM</a></li> </ul> </li> <li>Artificial intelligence <ul dir="auto"> <li><a href="https://www.aaai.org/" rel="nofollow">AAAI</a></li> <li><a href="https://www.aistats.org/" rel="nofollow">AISTATS</a></li> <li><a href="https://e-nns.org/icann2019/" rel="nofollow">ICANN</a></li> <li><a href="https://www.ijcai.org/" rel="nofollow">IJCAI</a></li> <li><a href="http://www.auai.org/" rel="nofollow">UAI</a></li> </ul> </li> </ul> <p dir="auto">Similar collections about <a href="https://github.com/benedekrozemberczki/awesome-graph-classification">graph classification</a>, <a href="https://github.com/benedekrozemberczki/awesome-decision-tree-papers">classification/regression tree</a>, <a href="https://github.com/benedekrozemberczki/awesome-fraud-detection-papers">fraud detection</a>, <a href="https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers">Monte Carlo tree search</a>, and <a href="https://github.com/benedekrozemberczki/awesome-community-detection">community detection</a> papers with implementations.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2023</h2><a id="user-content-2023" class="anchor" aria-label="Permalink: 2023" href="#2023"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Computing Abductive Explanations for Boosted Trees (AISTATS 2023)</strong></p> <ul dir="auto"> <li>Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski</li> <li><a href="https://arxiv.org/abs/2209.07740" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Off-Policy Learning (AISTATS 2023)</strong></p> <ul dir="auto"> <li>Ben London, Levi Lu, Ted Sandler, Thorsten Joachims</li> <li><a href="https://arxiv.org/abs/2208.01148" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Variational Boosted Soft Trees (AISTATS 2023)</strong></p> <ul dir="auto"> <li>Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov</li> <li><a href="https://arxiv.org/abs/2302.10706" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Krylov-Bellman boosting: Super-linear policy evaluation in general state spaces (AISTATS 2023)</strong></p> <ul dir="auto"> <li>Eric Xia, Martin J. Wainwright</li> <li><a href="https://arxiv.org/abs/2210.11377" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>FairGBM: Gradient Boosting with Fairness Constraints (ICLR 2023)</strong></p> <ul dir="auto"> <li>André Ferreira Cruz, Catarina Belém, João Bravo, Pedro Saleiro, Pedro Bizarro</li> <li><a href="https://arxiv.org/abs/2209.07850" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Gradient Boosting Performs Gaussian Process Inference (ICLR 2023)</strong></p> <ul dir="auto"> <li>Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova</li> <li><a href="https://arxiv.org/abs/2206.05608" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2022</h2><a id="user-content-2022" class="anchor" aria-label="Permalink: 2022" href="#2022"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion (AAAI 2022)</strong></p> <ul dir="auto"> <li>Yiheng Sun, Tian Lu, Cong Wang, Yuan Li, Huaiyu Fu, Jingran Dong, Yunjie Xu</li> <li><a href="https://arxiv.org/abs/2112.02365" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Resilient Distributed Boosting Algorithm (ICML 2022)</strong></p> <ul dir="auto"> <li>Yuval Filmus, Idan Mehalel, Shay Moran</li> <li><a href="https://arxiv.org/abs/2206.04713" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Fast Provably Robust Decision Trees and Boosting (ICML 2022)</strong></p> <ul dir="auto"> <li>Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou</li> <li><a href="https://proceedings.mlr.press/v162/guo22h.html" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Building Robust Ensembles via Margin Boosting (ICML 2022)</strong></p> <ul dir="auto"> <li>Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala</li> <li><a href="https://arxiv.org/abs/2206.03362" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Retrieval-Based Gradient Boosting Decision Trees for Disease Risk Assessment (KDD 2022)</strong></p> <ul dir="auto"> <li>Handong Ma, Jiahang Cao, Yuchen Fang, Weinan Zhang, Wenbo Sheng, Shaodian Zhang, Yong Yu</li> <li><a href="https://dl.acm.org/doi/abs/10.1145/3534678.3539052" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Federated Functional Gradient Boosting (AISTATS 2022)</strong></p> <ul dir="auto"> <li>Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi</li> <li><a href="https://arxiv.org/abs/2103.06972" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics (AISTATS 2022)</strong></p> <ul dir="auto"> <li>Daniel Csillag, Carolina Piazza, Thiago Ramos, João Vitor Romano, Roberto I. Oliveira, Paulo Orenstein</li> <li><a href="https://proceedings.mlr.press/v151/csillag22a.html" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2021</h2><a id="user-content-2021" class="anchor" aria-label="Permalink: 2021" href="#2021"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Precision-based Boosting (AAAI 2021)</strong></p> <ul dir="auto"> <li>Mohammad Hossein Nikravan, Marjan Movahedan, Sandra Zilles</li> <li><a href="https://ojs.aaai.org/index.php/AAAI/article/view/17105" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>BNN: Boosting Neural Network Framework Utilizing Limited Amount of Data (CIKM 2021)</strong></p> <ul dir="auto"> <li>Amit Livne, Roy Dor, Bracha Shapira, Lior Rokach</li> <li><a href="https://dl.acm.org/doi/abs/10.1145/3459637.3482414" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Unsupervised Domain Adaptation for Static Malware Detection based on Gradient Boosting Trees (CIKM 2021)</strong></p> <ul dir="auto"> <li>Panpan Qi, Wei Wang, Lei Zhu, See-Kiong Ng</li> <li><a href="https://dl.acm.org/doi/pdf/10.1145/3459637.3482400" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Individually Fair Gradient Boosting (ICLR 2021)</strong></p> <ul dir="auto"> <li>Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun</li> <li><a href="https://arxiv.org/abs/2103.16785" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees (ICLR 2021)</strong></p> <ul dir="auto"> <li>Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork</li> <li><a href="https://iclr.cc/virtual/2021/spotlight/3536" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models (ICLR 2021)</strong></p> <ul dir="auto"> <li>Ke Sun, Zhanxing Zhu, Zhouchen Lin</li> <li><a href="https://arxiv.org/abs/1908.05081" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/datake/AdaGCN">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Uncertainty in Gradient Boosting via Ensembles (ICLR 2021)</strong></p> <ul dir="auto"> <li>Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko</li> <li><a href="https://arxiv.org/abs/2006.10562" rel="nofollow">[Paper]</a></li> <li></li> </ul> </li> <li> <p dir="auto"><strong>Boost then Convolve: Gradient Boosting Meets Graph Neural Networks (ICLR 2021)</strong></p> <ul dir="auto"> <li>Sergei Ivanov, Liudmila Prokhorenkova</li> <li><a href="https://arxiv.org/abs/2101.08543" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>GBHT: Gradient Boosting Histogram Transform for Density Estimation (ICML 2021)</strong></p> <ul dir="auto"> <li>Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin</li> <li><a href="https://arxiv.org/abs/2106.05738" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting for Online Convex Optimization (ICML 2021)</strong></p> <ul dir="auto"> <li>Elad Hazan, Karan Singh</li> <li><a href="https://arxiv.org/abs/2102.09305" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Accuracy, Interpretability, and Differential Privacy via Explainable Boosting (ICML 2021)</strong></p> <ul dir="auto"> <li>Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni</li> <li><a href="https://arxiv.org/abs/2106.09680" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>SGLB: Stochastic Gradient Langevin Boosting (ICML 2021)</strong></p> <ul dir="auto"> <li>Aleksei Ustimenko, Liudmila Prokhorenkova</li> <li><a href="https://arxiv.org/abs/2001.07248" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Self-boosting for Feature Distillation (IJCAI 2021)</strong></p> <ul dir="auto"> <li>Yulong Pei, Yanyun Qu, Junping Zhang</li> <li><a href="https://www.ijcai.org/proceedings/2021/131" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Variational Inference With Locally Adaptive Step-Sizes (IJCAI 2021)</strong></p> <ul dir="auto"> <li>Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch</li> <li><a href="https://arxiv.org/abs/2105.09240" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression (KDD 2021)</strong></p> <ul dir="auto"> <li>Olivier Sprangers, Sebastian Schelter, Maarten de Rijke</li> <li><a href="https://arxiv.org/abs/2106.01682" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction (KDD 2021)</strong></p> <ul dir="auto"> <li>Mingcheng Chen, Zhenghui Wang, Zhiyun Zhao, Weinan Zhang, Xiawei Guo, Jian Shen, Yanru Qu, Jieli Lu, Min Xu, Yu Xu, Tiange Wang, Mian Li, Weiwei Tu, Yong Yu, Yufang Bi, Weiqing Wang, Guang Ning</li> <li><a href="https://arxiv.org/abs/2108.07107" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Better Short than Greedy: Interpretable Models through Optimal Rule Boosting (SDM 2021)</strong></p> <ul dir="auto"> <li>Mario Boley, Simon Teshuva, Pierre Le Bodic, Geoffrey I. Webb</li> <li><a href="https://arxiv.org/abs/2101.08380" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2020</h2><a id="user-content-2020" class="anchor" aria-label="Permalink: 2020" href="#2020"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains (AAAI 2020)</strong></p> <ul dir="auto"> <li>Harsha Kokel, Phillip Odom, Shuo Yang, Sriraam Natarajan</li> <li><a href="https://personal.utdallas.edu/~sriraam.natarajan/Papers/Kokel_AAAI20.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/harshakokel/KiGB">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Practical Federated Gradient Boosting Decision Trees (AAAI 2020)</strong></p> <ul dir="auto"> <li>Qinbin Li, Zeyi Wen, Bingsheng He</li> <li><a href="https://arxiv.org/abs/1911.04206" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Privacy-Preserving Gradient Boosting Decision Trees (AAAI 2020)</strong></p> <ul dir="auto"> <li>Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He</li> <li><a href="https://arxiv.org/abs/1911.04209" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Accelerating Gradient Boosting Machines (AISTATS 2020)</strong></p> <ul dir="auto"> <li>Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni</li> <li><a href="https://arxiv.org/abs/1903.08708" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Scalable Feature Selection for Multitask Gradient Boosted Trees (AISTATS 2020)</strong></p> <ul dir="auto"> <li>Cuize Han, Nikhil Rao, Daria Sorokina, Karthik Subbian</li> <li><a href="http://proceedings.mlr.press/v108/han20a.html" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees (AISTATS 2020)</strong></p> <ul dir="auto"> <li>Atsushi Nitanda, Taiji Suzuki</li> <li><a href="http://proceedings.mlr.press/v108/nitanda20a.html" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Learning Optimal Decision Trees with MaxSAT and its Integration in AdaBoost (IJCAI 2020)</strong></p> <ul dir="auto"> <li>Hao Hu, Mohamed Siala, Emmanuel Hebrard, Marie-José Huguet</li> <li><a href="https://www.ijcai.org/Proceedings/2020/163" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>MixBoost: Synthetic Oversampling using Boosted Mixup for Handling Extreme Imbalance (ICDM 2020)</strong></p> <ul dir="auto"> <li>Anubha Kabra, Ayush Chopra, Nikaash Puri, Pinkesh Badjatiya, Sukriti Verma, Piyush Gupta, Balaji Krishnamurthy</li> <li><a href="https://arxiv.org/abs/2009.01571" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting for Control of Dynamical Systems (ICML 2020)</strong></p> <ul dir="auto"> <li>Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu</li> <li><a href="https://arxiv.org/abs/1906.08720" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Quantum Boosting (ICML 2020)</strong></p> <ul dir="auto"> <li>Srinivasan Arunachalam, Reevu Maity</li> <li><a href="https://arxiv.org/abs/2002.05056" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Histogram Transform for Regression (ICML 2020)</strong></p> <ul dir="auto"> <li>Yuchao Cai, Hanyuan Hang, Hanfang Yang, Zhouchen Lin</li> <li><a href="https://proceedings.icml.cc/static/paper_files/icml/2020/2360-Paper.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Frank-Wolfe by Chasing Gradients (ICML 2020)</strong></p> <ul dir="auto"> <li>Cyrille W. Combettes, Sebastian Pokutta</li> <li><a href="https://arxiv.org/abs/2003.06369" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>NGBoost: Natural Gradient Boosting for Probabilistic Prediction (ICML 2020)</strong></p> <ul dir="auto"> <li>Tony Duan, Avati Anand, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler</li> <li><a href="https://arxiv.org/abs/1910.03225" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/stanfordmlgroup/ngboost">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Online Agnostic Boosting via Regret Minimization (NeurIPS 2020)</strong></p> <ul dir="auto"> <li>Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran</li> <li><a href="https://arxiv.org/abs/2003.01150" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst Case Rates (NeurIPS 2020)</strong></p> <ul dir="auto"> <li>Kaiwen Zhou, Anthony Man-Cho So, James Cheng</li> <li><a href="https://arxiv.org/abs/2005.12061" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks (NeurIPS 2020)</strong></p> <ul dir="auto"> <li>Kenta Oono, Taiji Suzuki</li> <li><a href="https://arxiv.org/abs/2006.08550" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/delta2323/GB-GNN">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Gradient Boosted Normalizing Flows (NeurIPS 2020)</strong></p> <ul dir="auto"> <li>Robert Giaquinto, Arindam Banerjee</li> <li><a href="https://arxiv.org/abs/2002.11896" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/robert-giaquinto/gradient-boosted-normalizing-flows">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems (WSDM 2020)</strong></p> <ul dir="auto"> <li>Lucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, Xiaoli Li</li> <li><a href="https://arxiv.org/abs/1809.01703" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2019</h2><a id="user-content-2019" class="anchor" aria-label="Permalink: 2019" href="#2019"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME (AAAI 2019)</strong></p> <ul dir="auto"> <li>Farhad Shakerin, Gopal Gupta</li> <li><a href="https://arxiv.org/abs/1808.00629" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Verifying Robustness of Gradient Boosted Models (AAAI 2019)</strong></p> <ul dir="auto"> <li>Gil Einziger, Maayan Goldstein, Yaniv Sa'ar, Itai Segall</li> <li><a href="https://arxiv.org/pdf/1906.10991.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Online Multiclass Boosting with Bandit Feedback (AISTATS 2019)</strong></p> <ul dir="auto"> <li>Daniel T. Zhang, Young Hun Jung, Ambuj Tewari</li> <li><a href="https://arxiv.org/abs/1810.05290" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>AdaFair: Cumulative Fairness Adaptive Boosting (CIKM 2019)</strong></p> <ul dir="auto"> <li>Vasileios Iosifidis, Eirini Ntoutsi</li> <li><a href="https://arxiv.org/abs/1909.08982" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Interpretable MTL from Heterogeneous Domains using Boosted Tree (CIKM 2019)</strong></p> <ul dir="auto"> <li>Ya-Lin Zhang, Longfei Li</li> <li><a href="https://dl.acm.org/citation.cfm?id=3357384.3358072" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Adversarial Training of Gradient-Boosted Decision Trees (CIKM 2019)</strong></p> <ul dir="auto"> <li>Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei</li> <li><a href="https://www.dais.unive.it/~calzavara/papers/cikm19.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Fair Adversarial Gradient Tree Boosting (ICDM 2019)</strong></p> <ul dir="auto"> <li>Vincent Grari, Boris Ruf, Sylvain Lamprier, Marcin Detyniecki</li> <li><a href="https://arxiv.org/abs/1911.05369" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Density Estimation Remastered (ICML 2019)</strong></p> <ul dir="auto"> <li>Zac Cranko, Richard Nock</li> <li><a href="https://arxiv.org/abs/1803.08178" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Lossless or Quantized Boosting with Integer Arithmetic (ICML 2019)</strong></p> <ul dir="auto"> <li>Richard Nock, Robert C. Williamson</li> <li><a href="http://proceedings.mlr.press/v97/nock19a.html" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Optimal Minimal Margin Maximization with Boosting (ICML 2019)</strong></p> <ul dir="auto"> <li>Alexander Mathiasen, Kasper Green Larsen, Allan Grønlund</li> <li><a href="https://arxiv.org/abs/1901.10789" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number (ICML 2019)</strong></p> <ul dir="auto"> <li>Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang</li> <li><a href="https://arxiv.org/abs/1809.06754" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting for Comparison-Based Learning (IJCAI 2019)</strong></p> <ul dir="auto"> <li>Michaël Perrot, Ulrike von Luxburg</li> <li><a href="https://arxiv.org/abs/1810.13333" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>AugBoost: Gradient Boosting Enhanced with Step-Wise Feature Augmentation (IJCAI 2019)</strong></p> <ul dir="auto"> <li>Philip Tannor, Lior Rokach</li> <li><a href="https://www.ijcai.org/proceedings/2019/0493.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Gradient Boosting with Piece-Wise Linear Regression Trees (IJCAI 2019)</strong></p> <ul dir="auto"> <li>Yu Shi, Jian Li, Zhize Li</li> <li><a href="https://arxiv.org/abs/1802.05640" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/GBDT-PL/GBDT-PL">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>SpiderBoost and Momentum: Faster Variance Reduction Algorithms (NeurIPS 2019)</strong></p> <ul dir="auto"> <li>Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh</li> <li><a href="http://papers.nips.cc/paper/8511-spiderboost-and-momentum-faster-variance-reduction-algorithms" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Faster Boosting with Smaller Memory (NeurIPS 2019)</strong></p> <ul dir="auto"> <li>Julaiti Alafate, Yoav Freund</li> <li><a href="https://arxiv.org/abs/1901.09047" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Regularized Gradient Boosting (NeurIPS 2019)</strong></p> <ul dir="auto"> <li>Corinna Cortes, Mehryar Mohri, Dmitry Storcheus</li> <li><a href="https://papers.nips.cc/paper/8784-regularized-gradient-boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Margin-Based Generalization Lower Bounds for Boosted Classifiers (NeurIPS 2019)</strong></p> <ul dir="auto"> <li>Allan Grønlund, Lior Kamma, Kasper Green Larsen, Alexander Mathiasen, Jelani Nelson</li> <li><a href="https://arxiv.org/abs/1909.12518" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Minimal Variance Sampling in Stochastic Gradient Boosting (NeurIPS 2019)</strong></p> <ul dir="auto"> <li>Bulat Ibragimov, Gleb Gusev</li> <li><a href="https://papers.nips.cc/paper/9645-minimal-variance-sampling-in-stochastic-gradient-boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Universal Boosting Variational Inference (NeurIPS 2019)</strong></p> <ul dir="auto"> <li>Trevor Campbell, Xinglong Li</li> <li><a href="https://arxiv.org/abs/1906.01235" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks (NeurIPS 2019)</strong></p> <ul dir="auto"> <li>Maksym Andriushchenko, Matthias Hein</li> <li><a href="https://arxiv.org/abs/1906.03526" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/max-andr/provably-robust-boosting">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Block-distributed Gradient Boosted Trees (SIGIR 2019)</strong></p> <ul dir="auto"> <li>Theodore Vasiloudis, Hyunsu Cho, Henrik Boström</li> <li><a href="https://arxiv.org/abs/1904.10522" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning (SIGIR 2019)</strong></p> <ul dir="auto"> <li>Claudio Lucchese, Franco Maria Nardini, Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang, Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke</li> <li><a href="https://www.researchgate.net/publication/334579610_Learning_to_Rank_in_Theory_and_Practice_From_Gradient_Boosting_to_Neural_Networks_and_Unbiased_Learning" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2018</h2><a id="user-content-2018" class="anchor" aria-label="Permalink: 2018" href="#2018"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Boosted Generative Models (AAAI 2018)</strong></p> <ul dir="auto"> <li>Aditya Grover, Stefano Ermon</li> <li><a href="https://arxiv.org/pdf/1702.08484.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/ermongroup/bgm">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Variational Inference: an Optimization Perspective (AISTATS 2018)</strong></p> <ul dir="auto"> <li>Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch</li> <li><a href="https://arxiv.org/abs/1708.01733" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/ratschlab/boosting-bbvi">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Online Boosting Algorithms for Multi-label Ranking (AISTATS 2018)</strong></p> <ul dir="auto"> <li>Young Hun Jung, Ambuj Tewari</li> <li><a href="https://arxiv.org/abs/1710.08079" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/yhjung88/OnlineMLRBoostingWithVFDT">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>DualBoost: Handling Missing Values with Feature Weights and Weak Classifiers that Abstain (CIKM 2018)</strong></p> <ul dir="auto"> <li>Weihong Wang, Jie Xu, Yang Wang, Chen Cai, Fang Chen</li> <li><a href="http://delivery.acm.org/10.1145/3270000/3269319/p1543-wang.pdf?ip=129.215.164.203&id=3269319&acc=ACTIVE%20SERVICE&key=C2D842D97AC95F7A%2EEB9E991028F4E1F1%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1558633895_f01b39fd47b943fd01eade763a397e04" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Functional Gradient Boosting based on Residual Network Perception (ICML 2018)</strong></p> <ul dir="auto"> <li>Atsushi Nitanda, Taiji Suzuki</li> <li><a href="https://arxiv.org/abs/1802.09031" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/anitan0925/ResFGB">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Finding Influential Training Samples for Gradient Boosted Decision Trees (ICML 2018)</strong></p> <ul dir="auto"> <li>Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, Maarten de Rijke</li> <li><a href="https://arxiv.org/abs/1802.06640" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Learning Deep ResNet Blocks Sequentially using Boosting Theory (ICML 2018)</strong></p> <ul dir="auto"> <li>Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire</li> <li><a href="https://arxiv.org/abs/1706.04964" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/JordanAsh/boostresnet">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>UCBoost: A Boosting Approach to Tame Complexity and Optimality for Stochastic Bandits (IJCAI 2018)</strong></p> <ul dir="auto"> <li>Fang Liu, Sinong Wang, Swapna Buccapatnam, Ness B. Shroff</li> <li><a href="https://www.ijcai.org/proceedings/2018/0338.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://smpybandits.github.io/docs/Policies.UCBoost.html" rel="nofollow">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Adaboost with Auto-Evaluation for Conversational Models (IJCAI 2018)</strong></p> <ul dir="auto"> <li>Juncen Li, Ping Luo, Ganbin Zhou, Fen Lin, Cheng Niu</li> <li><a href="https://www.ijcai.org/proceedings/2018/0580.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Ensemble Neural Relation Extraction with Adaptive Boosting (IJCAI 2018)</strong></p> <ul dir="auto"> <li>Dongdong Yang, Senzhang Wang, Zhoujun Li</li> <li><a href="https://www.ijcai.org/proceedings/2018/0630.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>CatBoost: Unbiased Boosting with Categorical Features (NIPS 2018)</strong></p> <ul dir="auto"> <li>Liudmila Ostroumova Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin</li> <li><a href="https://papers.nips.cc/paper/7898-catboost-unbiased-boosting-with-categorical-features.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/catboost/catboost">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Multitask Boosting for Survival Analysis with Competing Risks (NIPS 2018)</strong></p> <ul dir="auto"> <li>Alexis Bellot, Mihaela van der Schaar</li> <li><a href="https://papers.nips.cc/paper/7413-multitask-boosting-for-survival-analysis-with-competing-risks" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Multi-Layered Gradient Boosting Decision Trees (NIPS 2018)</strong></p> <ul dir="auto"> <li>Ji Feng, Yang Yu, Zhi-Hua Zhou</li> <li><a href="https://papers.nips.cc/paper/7614-multi-layered-gradient-boosting-decision-trees.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/kingfengji/mGBDT">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Sparse and Low-Rank Tensor Regression (NIPS 2018)</strong></p> <ul dir="auto"> <li>Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang</li> <li><a href="https://arxiv.org/abs/1811.01158" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/LifangHe/NeurIPS18_SURF">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Selective Gradient Boosting for Effective Learning to Rank (SIGIR 2018)</strong></p> <ul dir="auto"> <li>Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, Salvatore Orlando, Salvatore Trani</li> <li><a href="http://quickrank.isti.cnr.it/selective-data/selective-SIGIR2018.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/hpclab/quickrank/blob/master/documentation/selective.md">[Code]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2017</h2><a id="user-content-2017" class="anchor" aria-label="Permalink: 2017" href="#2017"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Boosting for Real-Time Multivariate Time Series Classification (AAAI 2017)</strong></p> <ul dir="auto"> <li>Haishuai Wang, Jun Wu</li> <li><a href="https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14852/14241" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Cross-Domain Sentiment Classification via Topic-Related TrAdaBoost (AAAI 2017)</strong></p> <ul dir="auto"> <li>Xingchang Huang, Yanghui Rao, Haoran Xie, Tak-Lam Wong, Fu Lee Wang</li> <li><a href="https://pdfs.semanticscholar.org/826c/c83d98a5c4c7dcc02be1f4dd9c27e2b99670.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/xchhuang/cross-domain-sentiment-classification">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Extreme Gradient Boosting and Behavioral Biometrics (AAAI 2017)</strong></p> <ul dir="auto"> <li>Benjamin Manning</li> <li><a href="https://pdfs.semanticscholar.org/8c6e/6c887d6d47dda3f0c73297fd4da516fef1ee.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>FeaBoost: Joint Feature and Label Refinement for Semantic Segmentation (AAAI 2017)</strong></p> <ul dir="auto"> <li>Yulei Niu, Zhiwu Lu, Songfang Huang, Xin Gao, Ji-Rong Wen</li> <li><a href="https://pdfs.semanticscholar.org/d566/73be998b3ed38ccbb53551e38758ae8cfc9d.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Complementary Hash Tables for Fast Nearest Neighbor Search (AAAI 2017)</strong></p> <ul dir="auto"> <li>Xianglong Liu, Cheng Deng, Yadong Mu, Zhujin Li</li> <li><a href="https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14336" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Gradient Boosting on Stochastic Data Streams (AISTATS 2017)</strong></p> <ul dir="auto"> <li>Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell</li> <li><a href="https://arxiv.org/abs/1703.00377" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>BoostVHT: Boosting Distributed Streaming Decision Trees (CIKM 2017)</strong></p> <ul dir="auto"> <li>Theodore Vasiloudis, Foteini Beligianni, Gianmarco De Francisci Morales</li> <li><a href="https://melmeric.files.wordpress.com/2010/05/boostvht-boosting-distributed-streaming-decision-trees.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Fast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features (CVPR 2017)</strong></p> <ul dir="auto"> <li>Arthur Daniel Costea, Robert Varga, Sergiu Nedevschi</li> <li><a href="http://openaccess.thecvf.com/content_cvpr_2017/papers/Costea_Fast_Boosting_Based_CVPR_2017_paper.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>BIER - Boosting Independent Embeddings Robustly (ICCV 2017)</strong></p> <ul dir="auto"> <li>Michael Opitz, Georg Waltner, Horst Possegger, Horst Bischof</li> <li><a href="http://openaccess.thecvf.com/content_ICCV_2017/papers/Opitz_BIER_-_Boosting_ICCV_2017_paper.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/mop/bier">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>An Analysis of Boosted Linear Classifiers on Noisy Data with Applications to Multiple-Instance Learning (ICDM 2017)</strong></p> <ul dir="auto"> <li>Rui Liu, Soumya Ray</li> <li><a href="https://ieeexplore.ieee.org/document/8215501" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Variational Boosting: Iteratively Refining Posterior Approximations (ICML 2017)</strong></p> <ul dir="auto"> <li>Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams</li> <li><a href="https://arxiv.org/abs/1611.06585" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/andymiller/vboost">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Fitted Q-Iteration (ICML 2017)</strong></p> <ul dir="auto"> <li>Samuele Tosatto, Matteo Pirotta, Carlo D'Eramo, Marcello Restelli</li> <li><a href="http://proceedings.mlr.press/v70/tosatto17a.html" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency (ICML 2017)</strong></p> <ul dir="auto"> <li>Ron Appel, Pietro Perona</li> <li><a href="http://proceedings.mlr.press/v70/appel17a.html" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/GuillaumeCollin/A-Simple-Multi-Class-Boosting-Framework-with-Theoretical-Guarantees-and-Empirical-Proficiency">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Gradient Boosted Decision Trees for High Dimensional Sparse Output (ICML 2017)</strong></p> <ul dir="auto"> <li>Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh</li> <li><a href="http://proceedings.mlr.press/v70/si17a.html" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/springdaisy/GBDT">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Local Topic Discovery via Boosted Ensemble of Nonnegative Matrix Factorization (IJCAI 2017)</strong></p> <ul dir="auto"> <li>Sangho Suh, Jaegul Choo, Joonseok Lee, Chandan K. Reddy</li> <li><a href="http://dmkd.cs.vt.edu/papers/IJCAI17.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/benedekrozemberczki/BoostedFactorization">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Zero-Shot Learning with Semantic Correlation Regularization (IJCAI 2017)</strong></p> <ul dir="auto"> <li>Te Pi, Xi Li, Zhongfei (Mark) Zhang</li> <li><a href="https://arxiv.org/abs/1707.08008" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency (KDD 2017)</strong></p> <ul dir="auto"> <li>Yin Lou, Mikhail Obukhov</li> <li><a href="https://yinlou.github.io/papers/lou-kdd17.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>CatBoost: Gradient Boosting with Categorical Features Support (NIPS 2017)</strong></p> <ul dir="auto"> <li>Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin</li> <li><a href="https://arxiv.org/abs/1810.11363" rel="nofollow">[Paper]</a></li> <li><a href="https://catboost.ai/" rel="nofollow">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Cost Efficient Gradient Boosting (NIPS 2017)</strong></p> <ul dir="auto"> <li>Sven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler</li> <li><a href="https://papers.nips.cc/paper/6753-cost-efficient-gradient-boosting" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/svenpeter42/LightGBM-CEGB">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>AdaGAN: Boosting Generative Models (NIPS 2017)</strong></p> <ul dir="auto"> <li>Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf</li> <li><a href="https://arxiv.org/abs/1701.02386" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/tolstikhin/adagan">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>LightGBM: A Highly Efficient Gradient Boosting Decision Tree (NIPS 2017)</strong></p> <ul dir="auto"> <li>Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu</li> <li><a href="https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree" rel="nofollow">[Paper]</a></li> <li><a href="https://lightgbm.readthedocs.io/en/latest/" rel="nofollow">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Early Stopping for Kernel Boosting Algorithms: A General Analysis with Localized Complexities (NIPS 2017)</strong></p> <ul dir="auto"> <li>Yuting Wei, Fanny Yang, Martin J. Wainwright</li> <li><a href="https://arxiv.org/abs/1707.01543" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/fanny-yang/EarlyStoppingRKHS">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Online Multiclass Boosting (NIPS 2017)</strong></p> <ul dir="auto"> <li>Young Hun Jung, Jack Goetz, Ambuj Tewari</li> <li><a href="https://papers.nips.cc/paper/6693-online-multiclass-boosting.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Stacking Bagged and Boosted Forests for Effective Automated Classification (SIGIR 2017)</strong></p> <ul dir="auto"> <li>Raphael R. Campos, Sérgio D. Canuto, Thiago Salles, Clebson C. A. de Sá, Marcos André Gonçalves</li> <li><a href="https://homepages.dcc.ufmg.br/~rcampos/papers/sigir2017/appendix.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/raphaelcampos/stacking-bagged-boosted-forests">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees (WWW 2017)</strong></p> <ul dir="auto"> <li>Qian Zhao, Yue Shi, Liangjie Hong</li> <li><a href="http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1311.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/grouplens/samantha">[Code]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2016</h2><a id="user-content-2016" class="anchor" aria-label="Permalink: 2016" href="#2016"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Group Cost-Sensitive Boosting for Multi-Resolution Pedestrian Detection (AAAI 2016)</strong></p> <ul dir="auto"> <li>Chao Zhu, Yuxin Peng</li> <li><a href="https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewFile/11898/12146" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/nnikolaou/Cost-sensitive-Boosting-Tutorial">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Communication Efficient Distributed Agnostic Boosting (AISTATS 2016)</strong></p> <ul dir="auto"> <li>Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau</li> <li><a href="https://arxiv.org/abs/1506.06318" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Logistic Boosting Regression for Label Distribution Learning (CVPR 2016)</strong></p> <ul dir="auto"> <li>Chao Xing, Xin Geng, Hui Xue</li> <li><a href="https://zpascal.net/cvpr2016/Xing_Logistic_Boosting_Regression_CVPR_2016_paper.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Structured Regression Gradient Boosting (CVPR 2016)</strong></p> <ul dir="auto"> <li>Ferran Diego, Fred A. Hamprecht</li> <li><a href="https://hci.iwr.uni-heidelberg.de/sites/default/files/publications/files/1037872734/diego_16_structured.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>L-EnsNMF: Boosted Local Topic Discovery via Ensemble of Nonnegative Matrix Factorization (ICDM 2016)</strong></p> <ul dir="auto"> <li>Sangho Suh, Jaegul Choo, Joonseok Lee, Chandan K. Reddy</li> <li><a href="https://ieeexplore.ieee.org/document/7837872" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/benedekrozemberczki/BoostedFactorization">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning (ICML 2016)</strong></p> <ul dir="auto"> <li>Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov</li> <li><a href="http://proceedings.mlr.press/v48/ustinovskiy16.html" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Generalized Dictionary for Multitask Learning with Boosting (IJCAI 2016)</strong></p> <ul dir="auto"> <li>Boyu Wang, Joelle Pineau</li> <li><a href="https://www.ijcai.org/Proceedings/16/Papers/299.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Self-Paced Boost Learning for Classification (IJCAI 2016)</strong></p> <ul dir="auto"> <li>Te Pi, Xi Li, Zhongfei Zhang, Deyu Meng, Fei Wu, Jun Xiao, Yueting Zhuang</li> <li><a href="https://pdfs.semanticscholar.org/31b6/ab4a0771d5b7405cacdd12c398b1c832729d.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Interactive Martingale Boosting (IJCAI 2016)</strong></p> <ul dir="auto"> <li>Ashish Kulkarni, Pushpak Burange, Ganesh Ramakrishnan</li> <li><a href="https://www.ijcai.org/Proceedings/16/Papers/124.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Optimal and Adaptive Algorithms for Online Boosting (IJCAI 2016)</strong></p> <ul dir="auto"> <li>Alina Beygelzimer, Satyen Kale, Haipeng Luo</li> <li><a href="https://www.ijcai.org/Proceedings/16/Papers/614.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/VowpalWabbit/vowpal_wabbit/blob/master/vowpalwabbit/boosting.cc">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews (IJCAI 2016)</strong></p> <ul dir="auto"> <li>Yunzhi Tan, Min Zhang, Yiqun Liu, Shaoping Ma</li> <li><a href="https://pdfs.semanticscholar.org/db63/89e0ca49ec0e4686e40604e7489cb4c0729d.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>XGBoost: A Scalable Tree Boosting System (KDD 2016)</strong></p> <ul dir="auto"> <li>Tianqi Chen, Carlos Guestrin</li> <li><a href="https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/dmlc/xgboost">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments (KDD 2016)</strong></p> <ul dir="auto"> <li>Alexey Poyarkov, Alexey Drutsa, Andrey Khalyavin, Gleb Gusev, Pavel Serdyukov</li> <li><a href="https://www.kdd.org/kdd2016/papers/files/adf0653-poyarkovA.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting with Abstention (NIPS 2016)</strong></p> <ul dir="auto"> <li>Corinna Cortes, Giulia DeSalvo, Mehryar Mohri</li> <li><a href="https://papers.nips.cc/paper/6336-boosting-with-abstention" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques (NIPS 2016)</strong></p> <ul dir="auto"> <li>Elad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky</li> <li><a href="https://papers.nips.cc/paper/6109-seboost-boosting-stochastic-learning-using-subspace-optimization-techniques.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/eladrich/seboost">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition (NIPS 2016)</strong></p> <ul dir="auto"> <li>Shizhong Han, Zibo Meng, Ahmed-Shehab Khan, Yan Tong</li> <li><a href="https://arxiv.org/abs/1707.05395" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/sjsingh91/IB-CNN">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Generalized BROOF-L2R: A General Framework for Learning to Rank Based on Boosting and Random Forests (SIGIR 2016)</strong></p> <ul dir="auto"> <li>Clebson C. A. de Sá, Marcos André Gonçalves, Daniel Xavier de Sousa, Thiago Salles</li> <li><a href="https://dl.acm.org/citation.cfm?id=2911540" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2015</h2><a id="user-content-2015" class="anchor" aria-label="Permalink: 2015" href="#2015"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Online Boosting Algorithms for Anytime Transfer and Multitask Learning (AAAI 2015)</strong></p> <ul dir="auto"> <li>Boyu Wang, Joelle Pineau</li> <li><a href="https://www.cs.mcgill.ca/~jpineau/files/bwang-aaai15.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Boosted Multi-Task Model for Pedestrian Detection with Occlusion Handling (AAAI 2015)</strong></p> <ul dir="auto"> <li>Chao Zhu, Yuxin Peng</li> <li><a href="https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9879/9825" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Efficient Second-Order Gradient Boosting for Conditional Random Fields (AISTATS 2015)</strong></p> <ul dir="auto"> <li>Tianqi Chen, Sameer Singh, Ben Taskar, Carlos Guestrin</li> <li><a href="http://proceedings.mlr.press/v38/chen15b.html" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Tumblr Blog Recommendation with Boosted Inductive Matrix Completion (CIKM 2015)</strong></p> <ul dir="auto"> <li>Donghyuk Shin, Suleyman Cetintas, Kuang-Chih Lee, Inderjit S. Dhillon</li> <li><a href="https://dl.acm.org/citation.cfm?id=2806578" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Basis mapping based boosting for object detection (CVPR 2015)</strong></p> <ul dir="auto"> <li>Haoyu Ren, Ze-Nian Li</li> <li><a href="https://ieeexplore.ieee.org/document/7298766" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Tracking-by-Segmentation with Online Gradient Boosting Decision Tree (ICCV 2015)</strong></p> <ul dir="auto"> <li>Jeany Son, Ilchae Jung, Kayoung Park, Bohyung Han</li> <li><a href="https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Son_Tracking-by-Segmentation_With_Online_ICCV_2015_paper.pdf" rel="nofollow">[Paper]</a></li> <li><a href="http://cvlab.postech.ac.kr/research/ogbdt_track/" rel="nofollow">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Learning to Boost Filamentary Structure Segmentation (ICCV 2015)</strong></p> <ul dir="auto"> <li>Lin Gu, Li Cheng</li> <li><a href="https://isg.nist.gov/BII_2015/webPages/pages/2015_BII_program/PDFs/Day_3/Session_9/Abstract_Gu_Lin.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Optimal and Adaptive Algorithms for Online Boosting (ICML 2015)</strong></p> <ul dir="auto"> <li>Alina Beygelzimer, Satyen Kale, Haipeng Luo</li> <li><a href="http://proceedings.mlr.press/v37/beygelzimer15.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/VowpalWabbit/vowpal_wabbit/blob/master/vowpalwabbit/boosting.cc">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Rademacher Observations, Private Data, and Boosting (ICML 2015)</strong></p> <ul dir="auto"> <li>Richard Nock, Giorgio Patrini, Arik Friedman</li> <li><a href="https://arxiv.org/abs/1502.02322" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions (ICML 2015)</strong></p> <ul dir="auto"> <li>Taehoon Lee, Sungroh Yoon</li> <li><a href="https://pdfs.semanticscholar.org/d0ad/beef3053e98dd88ff74f42744417bc65a729.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Direct Boosting Approach for Semi-supervised Classification (IJCAI 2015)</strong></p> <ul dir="auto"> <li>Shaodan Zhai, Tian Xia, Zhongliang Li, Shaojun Wang</li> <li><a href="https://www.ijcai.org/Proceedings/15/Papers/565.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Boosting Algorithm for Item Recommendation with Implicit Feedback (IJCAI 2015)</strong></p> <ul dir="auto"> <li>Yong Liu, Peilin Zhao, Aixin Sun, Chunyan Miao</li> <li><a href="https://www.ijcai.org/Proceedings/15/Papers/255.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/microsoft/recommenders">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Training-Time Optimization of a Budgeted Booster (IJCAI 2015)</strong></p> <ul dir="auto"> <li>Yi Huang, Brian Powers, Lev Reyzin</li> <li><a href="https://www.ijcai.org/Proceedings/15/Papers/504.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Optimal Action Extraction for Random Forests and Boosted Trees (KDD 2015)</strong></p> <ul dir="auto"> <li>Zhicheng Cui, Wenlin Chen, Yujie He, Yixin Chen</li> <li><a href="https://www.cse.wustl.edu/~ychen/public/OAE.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Online Gradient Boosting (NIPS 2015)</strong></p> <ul dir="auto"> <li>Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo</li> <li><a href="https://arxiv.org/abs/1506.04820" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/crm416/online_boosting">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>BROOF: Exploiting Out-of-Bag Errors Boosting and Random Forests for Effective Automated Classification (SIGIR 2015)</strong></p> <ul dir="auto"> <li>Thiago Salles, Marcos André Gonçalves, Victor Rodrigues, Leonardo C. da Rocha</li> <li><a href="https://homepages.dcc.ufmg.br/~tsalles/broof/appendix.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Search with Deep Understanding of Contents and Users (WSDM 2015)</strong></p> <ul dir="auto"> <li>Kaihua Zhu</li> <li><a href="https://www.researchgate.net/publication/282482189_Boosting_Search_with_Deep_Understanding_of_Contents_and_Users" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2014</h2><a id="user-content-2014" class="anchor" aria-label="Permalink: 2014" href="#2014"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>On Boosting Sparse Parities (AAAI 2014)</strong></p> <ul dir="auto"> <li>Lev Reyzin</li> <li><a href="https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8587" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis (CVPR 2014)</strong></p> <ul dir="auto"> <li>Yinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen</li> <li><a href="https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Shi_Joint_Coupled-Feature_Representation_2014_CVPR_paper.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>From Categories to Individuals in Real Time - A Unified Boosting Approach (CVPR 2014)</strong></p> <ul dir="auto"> <li>David Hall, Pietro Perona</li> <li><a href="https://ieeexplore.ieee.org/document/6909424" rel="nofollow">[Paper]</a></li> <li><a href="http://www.vision.caltech.edu/~dhall/projects/CategoriesToIndividuals/" rel="nofollow">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Efficient Boosted Exemplar-Based Face Detection (CVPR 2014)</strong></p> <ul dir="auto"> <li>Haoxiang Li, Zhe Lin, Jonathan Brandt, Xiaohui Shen, Gang Hua</li> <li><a href="http://users.eecs.northwestern.edu/~xsh835/assets/cvpr14_exemplarfacedetection.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Facial Expression Recognition via a Boosted Deep Belief Network (CVPR 2014)</strong></p> <ul dir="auto"> <li>Ping Liu, Shizhong Han, Zibo Meng, Yan Tong</li> <li><a href="https://ieeexplore.ieee.org/abstract/document/6909629" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Confidence-Rated Multiple Instance Boosting for Object Detection (CVPR 2014)</strong></p> <ul dir="auto"> <li>Karim Ali, Kate Saenko</li> <li><a href="https://ieeexplore.ieee.org/document/6909708" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>The Return of AdaBoost.MH: Multi-Class Hamming Trees (ICLR 2014)</strong></p> <ul dir="auto"> <li>Balázs Kégl</li> <li><a href="https://arxiv.org/pdf/1312.6086.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/aciditeam/acidano/blob/master/acidano/utils/cost.py">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Deep Boosting (ICML 2014)</strong></p> <ul dir="auto"> <li>Corinna Cortes, Mehryar Mohri, Umar Syed</li> <li><a href="http://proceedings.mlr.press/v32/cortesb14.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/google/deepboost">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Convergence Rate Analysis for LogitBoost, MART and Their Variant (ICML 2014)</strong></p> <ul dir="auto"> <li>Peng Sun, Tong Zhang, Jie Zhou</li> <li><a href="http://proceedings.mlr.press/v32/sunc14.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting with Online Binary Learners for the Multiclass Bandit Problem (ICML 2014)</strong></p> <ul dir="auto"> <li>Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu</li> <li><a href="https://www.cc.gatech.edu/~schen351/paper/icml14boost.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Multi-Step Autoregressive Forecasts (ICML 2014)</strong></p> <ul dir="auto"> <li>Souhaib Ben Taieb, Rob J. Hyndman</li> <li><a href="http://proceedings.mlr.press/v32/taieb14.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Dynamic Programming Boosting for Discriminative Macro-Action Discovery (ICML 2014)</strong></p> <ul dir="auto"> <li>Leonidas Lefakis, François Fleuret</li> <li><a href="http://proceedings.mlr.press/v32/lefakis14.html" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting (ICML 2014)</strong></p> <ul dir="auto"> <li>Oscar Beijbom, Mohammad J. Saberian, David J. Kriegman, Nuno Vasconcelos</li> <li><a href="http://proceedings.mlr.press/v32/beijbom14.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Multi-Class Boosting Method with Direct Optimization (KDD 2014)</strong></p> <ul dir="auto"> <li>Shaodan Zhai, Tian Xia, Shaojun Wang</li> <li><a href="https://dl.acm.org/citation.cfm?id=2623689" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Gradient Boosted Feature Selection (KDD 2014)</strong></p> <ul dir="auto"> <li>Zhixiang Eddie Xu, Gao Huang, Kilian Q. Weinberger, Alice X. Zheng</li> <li><a href="https://arxiv.org/abs/1901.04055" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/dmlc/xgboost">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Multi-Class Deep Boosting (NIPS 2014)</strong></p> <ul dir="auto"> <li>Vitaly Kuznetsov, Mehryar Mohri, Umar Syed</li> <li><a href="https://papers.nips.cc/paper/5514-multi-class-deep-boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Deconvolution of High Dimensional Mixtures via Boosting with Application to Diffusion-Weighted MRI of Human Brain (NIPS 2014)</strong></p> <ul dir="auto"> <li>Charles Y. Zheng, Franco Pestilli, Ariel Rokem</li> <li><a href="https://papers.nips.cc/paper/5506-deconvolution-of-high-dimensional-mixtures-via-boosting-with-application-to-diffusion-weighted-mri-of-human-brain" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Drifting-Games Analysis for Online Learning and Applications to Boosting (NIPS 2014)</strong></p> <ul dir="auto"> <li>Haipeng Luo, Robert E. Schapire</li> <li><a href="https://arxiv.org/abs/1406.1856" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Boosting Framework on Grounds of Online Learning (NIPS 2014)</strong></p> <ul dir="auto"> <li>Tofigh Naghibi Mohamadpoor, Beat Pfister</li> <li><a href="https://papers.nips.cc/paper/5512-a-boosting-framework-on-grounds-of-online-learning.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Gradient Boosting Factorization Machines (RECSYS 2014)</strong></p> <ul dir="auto"> <li>Chen Cheng, Fen Xia, Tong Zhang, Irwin King, Michael R. Lyu</li> <li><a href="http://tongzhang-ml.org/papers/recsys14-fm.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2013</h2><a id="user-content-2013" class="anchor" aria-label="Permalink: 2013" href="#2013"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Boosting Binary Keypoint Descriptors (CVPR 2013)</strong></p> <ul dir="auto"> <li>Tomasz Trzcinski, C. Mario Christoudias, Pascal Fua, Vincent Lepetit</li> <li><a href="https://cvlab.epfl.ch/research/page-90554-en-html/research-detect-binboost/" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/biotrump/cvlab-BINBOOST">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>PerturBoost: Practical Confidential Classifier Learning in the Cloud (ICDM 2013)</strong></p> <ul dir="auto"> <li>Keke Chen, Shumin Guo</li> <li><a href="https://ieeexplore.ieee.org/document/6729587" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Multiclass Semi-Supervised Boosting Using Similarity Learning (ICDM 2013)</strong></p> <ul dir="auto"> <li>Jafar Tanha, Mohammad Javad Saberian, Maarten van Someren</li> <li><a href="https://www.cse.msu.edu/~rongjin/publications/MultiClass-08.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner (ICML 2013)</strong></p> <ul dir="auto"> <li>Peng Sun, Jie Zhou</li> <li><a href="http://proceedings.mlr.press/v28/sun13.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>General Functional Matrix Factorization Using Gradient Boosting (ICML 2013)</strong></p> <ul dir="auto"> <li>Tianqi Chen, Hang Li, Qiang Yang, Yong Yu</li> <li><a href="http://w.hangli-hl.com/uploads/3/1/6/8/3168008/icml_2013.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Margins, Shrinkage, and Boosting (ICML 2013)</strong></p> <ul dir="auto"> <li>Matus Telgarsky</li> <li><a href="https://arxiv.org/abs/1303.4172" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Quickly Boosting Decision Trees - Pruning Underachieving Features Early (ICML 2013)</strong></p> <ul dir="auto"> <li>Ron Appel, Thomas J. Fuchs, Piotr Dollár, Pietro Perona</li> <li><a href="http://proceedings.mlr.press/v28/appel13.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/pdollar/toolbox/blob/master/classify/adaBoostTrain.m">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Human Boosting (ICML 2013)</strong></p> <ul dir="auto"> <li>Harsh H. Pareek, Pradeep Ravikumar</li> <li><a href="https://www.cs.cmu.edu/~pradeepr/paperz/humanboosting.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Collaborative Boosting for Activity Classification in Microblogs (KDD 2013)</strong></p> <ul dir="auto"> <li>Yangqiu Song, Zhengdong Lu, Cane Wing-ki Leung, Qiang Yang</li> <li><a href="http://chbrown.github.io/kdd-2013-usb/kdd/p482.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Direct 0-1 Loss Minimization and Margin Maximization with Boosting (NIPS 2013)</strong></p> <ul dir="auto"> <li>Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang</li> <li><a href="https://papers.nips.cc/paper/5214-direct-0-1-loss-minimization-and-margin-maximization-with-boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Reservoir Boosting : Between Online and Offline Ensemble Learning (NIPS 2013)</strong></p> <ul dir="auto"> <li>Leonidas Lefakis, François Fleuret</li> <li><a href="https://papers.nips.cc/paper/5215-reservoir-boosting-between-online-and-offline-ensemble-learning" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Non-Linear Domain Adaptation with Boosting (NIPS 2013)</strong></p> <ul dir="auto"> <li>Carlos J. Becker, C. Mario Christoudias, Pascal Fua</li> <li><a href="https://papers.nips.cc/paper/5200-non-linear-domain-adaptation-with-boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting in the Presence of Label Noise (UAI 2013)</strong></p> <ul dir="auto"> <li>Jakramate Bootkrajang, Ata Kabán</li> <li><a href="https://arxiv.org/abs/1309.6818" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2012</h2><a id="user-content-2012" class="anchor" aria-label="Permalink: 2012" href="#2012"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Contextual Boost for Pedestrian Detection (CVPR 2012)</strong></p> <ul dir="auto"> <li>Yuanyuan Ding, Jing Xiao</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.5611&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Shrink Boost for Selecting Multi-LBP Histogram Features in Object Detection (CVPR 2012)</strong></p> <ul dir="auto"> <li>Cher Keng Heng, Sumio Yokomitsu, Yuichi Matsumoto, Hajime Tamura</li> <li><a href="https://ieeexplore.ieee.org/document/6248061" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Bottom-Up and Top-Down Visual Features for Saliency Estimation (CVPR 2012)</strong></p> <ul dir="auto"> <li>Ali Borji</li> <li><a href="http://ilab.usc.edu/borji/papers/cvpr-2012-BUModel-v4.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Algorithms for Simultaneous Feature Extraction and Selection (CVPR 2012)</strong></p> <ul dir="auto"> <li>Mohammad J. Saberian, Nuno Vasconcelos</li> <li><a href="http://svcl.ucsd.edu/publications/conference/2012/cvpr/SOPBoost.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Sharing Features in Multi-class Boosting via Group Sparsity (CVPR 2012)</strong></p> <ul dir="auto"> <li>Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel</li> <li><a href="https://cs.adelaide.edu.au/~paulp/publications/pubs/sharing_cvpr2012.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Feature Weighting and Selection Using Hypothesis Margin of Boosting (ICDM 2012)</strong></p> <ul dir="auto"> <li>Malak Alshawabkeh, Javed A. Aslam, Jennifer G. Dy, David R. Kaeli</li> <li><a href="http://www.ece.neu.edu/fac-ece/jdy/papers/alshawabkeh-ICDM2012.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>An AdaBoost Algorithm for Multiclass Semi-supervised Learning (ICDM 2012)</strong></p> <ul dir="auto"> <li>Jafar Tanha, Maarten van Someren, Hamideh Afsarmanesh</li> <li>[[Paper]]<a href="https://ieeexplore.ieee.org/document/6413799" rel="nofollow">https://ieeexplore.ieee.org/document/6413799</a>)</li> </ul> </li> <li> <p dir="auto"><strong>AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem (ICML 2012)</strong></p> <ul dir="auto"> <li>Peng Sun, Mark D. Reid, Jie Zhou</li> <li>[[Paper]](AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem)</li> <li><a href="https://github.com/pengsun/AOSOLogitBoost">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>An Online Boosting Algorithm with Theoretical Justifications (ICML 2012)</strong></p> <ul dir="auto"> <li>Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu</li> <li><a href="https://arxiv.org/abs/1206.6422" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Learning Image Descriptors with the Boosting-Trick (NIPS 2012)</strong></p> <ul dir="auto"> <li>Tomasz Trzcinski, C. Mario Christoudias, Vincent Lepetit, Pascal Fua</li> <li><a href="https://papers.nips.cc/paper/4848-learning-image-descriptors-with-the-boosting-trick.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/biotrump/cvlab-BINBOOST">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Accelerated Training for Matrix-norm Regularization: A Boosting Approach (NIPS 2012)</strong></p> <ul dir="auto"> <li>Xinhua Zhang, Yaoliang Yu, Dale Schuurmans</li> <li><a href="https://papers.nips.cc/paper/4663-accelerated-training-for-matrix-norm-regularization-a-boosting-approach" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Learning from Heterogeneous Sources via Gradient Boosting Consensus (SDM 2012)</strong></p> <ul dir="auto"> <li>Xiaoxiao Shi, Jean-François Paiement, David Grangier, Philip S. Yu</li> <li><a href="http://david.grangier.info/papers/2012/shi_sdm_2012.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/PriyeshV/GBDT-CC">[Code]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2011</h2><a id="user-content-2011" class="anchor" aria-label="Permalink: 2011" href="#2011"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Selective Transfer Between Learning Tasks Using Task-Based Boosting (AAAI 2011)</strong></p> <ul dir="auto"> <li>Eric Eaton, Marie desJardins</li> <li><a href="http://www.cis.upenn.edu/~eeaton/papers/Eaton2011Selective.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Incorporating Boosted Regression Trees into Ecological Latent Variable Models (AAAI 2011)</strong></p> <ul dir="auto"> <li>Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich</li> <li><a href="https://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/viewFile/3711/4086" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>FlowBoost - Appearance Learning from Sparsely Annotated Video (CVPR 2011)</strong></p> <ul dir="auto"> <li>Karim Ali, David Hasler, François Fleuret</li> <li><a href="http://www.karimali.org/publications/AHF_CVPR11.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>AdaBoost on Low-Rank PSD Matrices for Metric Learning (CVPR 2011)</strong></p> <ul dir="auto"> <li>Jinbo Bi, Dijia Wu, Le Lu, Meizhu Liu, Yimo Tao, Matthias Wolf</li> <li><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995363" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Local Structured HOG-LBP for Object Localization (CVPR 2011)</strong></p> <ul dir="auto"> <li>Junge Zhang, Kaiqi Huang, Yinan Yu, Tieniu Tan</li> <li><a href="http://www.cbsr.ia.ac.cn/users/ynyu/1682.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Direct Formulation for Totally-Corrective Multi-Class Boosting (CVPR 2011)</strong></p> <ul dir="auto"> <li>Chunhua Shen, Zhihui Hao</li> <li><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5995554" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Gated Classifiers: Boosting Under High Intra-class Variation (CVPR 2011)</strong></p> <ul dir="auto"> <li>Oscar M. Danielsson, Babak Rasolzadeh, Stefan Carlsson</li> <li><a href="http://www.nada.kth.se/cvap/cvg/papers/danielssonCVPR11.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>TaylorBoost: First and Second-order Boosting Algorithms with Explicit Margin Control (CVPR 2011)</strong></p> <ul dir="auto"> <li>Mohammad J. Saberian, Hamed Masnadi-Shirazi, Nuno Vasconcelos</li> <li><a href="https://ieeexplore.ieee.org/document/5995605" rel="nofollow">[Paper]</a></li> <li><a href="https://pythonhosted.org/bob.learn.boosting/" rel="nofollow">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Robust and Efficient Regularized Boosting Using Total Bregman Divergence (CVPR 2011)</strong></p> <ul dir="auto"> <li>Meizhu Liu, Baba C. Vemuri</li> <li><a href="https://ieeexplore.ieee.org/document/5995686" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Treat Samples differently: Object Tracking with Semi-Supervised Online CovBoost (ICCV 2011)</strong></p> <ul dir="auto"> <li>Guorong Li, Lei Qin, Qingming Huang, Junbiao Pang, Shuqiang Jiang</li> <li><a href="https://ieeexplore.ieee.org/document/6126297" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>LinkBoost: A Novel Cost-Sensitive Boosting Framework for Community-Level Network Link Prediction (ICDM 2011)</strong></p> <ul dir="auto"> <li>Prakash Mandayam Comar, Pang-Ning Tan, Anil K. Jain</li> <li><a href="http://www.cse.msu.edu/~ptan/papers/icdm2011.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Learning Markov Logic Networks via Functional Gradient Boosting (ICDM 2011)</strong></p> <ul dir="auto"> <li>Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik</li> <li><a href="https://github.com/starling-lab/BoostSRL">[Paper]</a></li> <li><a href="https://ieeexplore.ieee.org/document/6137236" rel="nofollow">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting on a Budget: Sampling for Feature-Efficient Prediction (ICML 2011)</strong></p> <ul dir="auto"> <li>Lev Reyzin</li> <li><a href="http://www.icml-2011.org/papers/348_icmlpaper.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Multiclass Boosting with Hinge Loss based on Output Coding (ICML 2011)</strong></p> <ul dir="auto"> <li>Tianshi Gao, Daphne Koller</li> <li><a href="http://ai.stanford.edu/~tianshig/papers/multiclassHingeBoost-ICML2011.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/memect/hao/blob/master/awesome/multiclass-boosting.md">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Generalized Boosting Algorithms for Convex Optimization (ICML 2011)</strong></p> <ul dir="auto"> <li>Alexander Grubb, Drew Bagnell</li> <li><a href="https://arxiv.org/pdf/1105.2054.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach (IJCAI 2011)</strong></p> <ul dir="auto"> <li>Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik</li> <li><a href="http://ftp.cs.wisc.edu/machine-learning/shavlik-group/natarajan.ijcai11.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting with Maximum Adaptive Sampling (NIPS 2011)</strong></p> <ul dir="auto"> <li>Charles Dubout, François Fleuret</li> <li><a href="https://papers.nips.cc/paper/4310-boosting-with-maximum-adaptive-sampling" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>The Fast Convergence of Boosting (NIPS 2011)</strong></p> <ul dir="auto"> <li>Matus Telgarsky</li> <li><a href="https://papers.nips.cc/paper/4343-the-fast-convergence-of-boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>ShareBoost: Efficient Multiclass Learning with Feature Sharing (NIPS 2011)</strong></p> <ul dir="auto"> <li>Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua</li> <li><a href="https://papers.nips.cc/paper/4213-shareboost-efficient-multiclass-learning-with-feature-sharing" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Multiclass Boosting: Theory and Algorithms (NIPS 2011)</strong></p> <ul dir="auto"> <li>Mohammad J. Saberian, Nuno Vasconcelos</li> <li><a href="https://papers.nips.cc/paper/4450-multiclass-boosting-theory-and-algorithms.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Variance Penalizing AdaBoost (NIPS 2011)</strong></p> <ul dir="auto"> <li>Pannagadatta K. Shivaswamy, Tony Jebara</li> <li><a href="https://papers.nips.cc/paper/4207-variance-penalizing-adaboost.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>MKBoost: A Framework of Multiple Kernel Boosting (SDM 2011)</strong></p> <ul dir="auto"> <li>Hao Xia, Steven C. H. Hoi</li> <li><a href="https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3280&context=sis_research" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Boosting Approach to Improving Pseudo-Relevance Feedback (SIGIR 2011)</strong></p> <ul dir="auto"> <li>Yuanhua Lv, ChengXiang Zhai, Wan Chen</li> <li><a href="http://www.tyr.unlu.edu.ar/tallerIR/2012/papers/pseudorelevance.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Bagging Gradient-Boosted Trees for High Precision, Low Variance Ranking Models (SIGIR 2011)</strong></p> <ul dir="auto"> <li>Yasser Ganjisaffar, Rich Caruana, Cristina Videira Lopes</li> <li><a href="http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/materials/bagging_lmbamart_jforests.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting as a Product of Experts (UAI 2011)</strong></p> <ul dir="auto"> <li>Narayanan Unny Edakunni, Gary Brown, Tim Kovacs</li> <li><a href="https://arxiv.org/abs/1202.3716" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Parallel Boosted Regression Trees for Web Search Ranking (WWW 2011)</strong></p> <ul dir="auto"> <li>Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, Jennifer Paykin</li> <li><a href="http://www.cs.cornell.edu/~kilian/papers/fr819-tyreeA.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/YS-L/pgbm">[Code]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2010</h2><a id="user-content-2010" class="anchor" aria-label="Permalink: 2010" href="#2010"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>The Boosting Effect of Exploratory Behaviors (AAAI 2010)</strong></p> <ul dir="auto"> <li>Jivko Sinapov, Alexander Stoytchev</li> <li><a href="https://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/download/1777/2265" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting-Based System Combination for Machine Translation (ACL 2010)</strong></p> <ul dir="auto"> <li>Tong Xiao, Jingbo Zhu, Muhua Zhu, Huizhen Wang</li> <li><a href="https://www.aclweb.org/anthology/P10-1076" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>BagBoo: A Scalable Hybrid Bagging-the-Boosting Model (CIKM 2010)</strong></p> <ul dir="auto"> <li>Dmitry Yurievich Pavlov, Alexey Gorodilov, Cliff A. Brunk</li> <li><a href="http://cache-default03h.cdn.yandex.net/download.yandex.ru/company/a_scalable_hybrid_bagging_the_boosting_model.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/arogozhnikov/infiniteboost">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Automatic Detection of Craters in Planetary Images: an Embedded Framework Using Feature Selection and Boosting (CIKM 2010)</strong></p> <ul dir="auto"> <li>Wei Ding, Tomasz F. Stepinski, Lourenço P. C. Bandeira, Ricardo Vilalta, Youxi Wu, Zhenyu Lu, Tianyu Cao</li> <li><a href="https://www.uh.edu/~rvilalta/papers/2010/cikm10.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Facial Point Detection Using Boosted Regression and Graph Models (CVPR 2010)</strong></p> <ul dir="auto"> <li>Michel François Valstar, Brais Martínez, Xavier Binefa, Maja Pantic</li> <li><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/CVPR-2010-ValstarEtAl-CAMERA.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting for Transfer Learning with Multiple Sources (CVPR 2010)</strong></p> <ul dir="auto"> <li>Yi Yao, Gianfranco Doretto</li> <li><a href="https://ieeexplore.ieee.org/document/5539857" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Efficient Rotation Invariant Object Detection Using Boosted Random Ferns (CVPR 2010)</strong></p> <ul dir="auto"> <li>Michael Villamizar, Francesc Moreno-Noguer, Juan Andrade-Cetto, Alberto Sanfeliu</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.307.4002&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Implicit Hierarchical Boosting for Multi-view Object Detection (CVPR 2010)</strong></p> <ul dir="auto"> <li>Xavier Perrotton, Marc Sturzel, Michel Roux</li> <li><a href="https://ieeexplore.ieee.org/document/5540115" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>On-Line Semi-Supervised Multiple-Instance Boosting (CVPR 2010)</strong></p> <ul dir="auto"> <li>Bernhard Zeisl, Christian Leistner, Amir Saffari, Horst Bischof</li> <li><a href="https://ieeexplore.ieee.org/document/5539860" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Online Multi-Class LPBoost (CVPR 2010)</strong></p> <ul dir="auto"> <li>Amir Saffari, Martin Godec, Thomas Pock, Christian Leistner, Horst Bischof</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.165.8939&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/amirsaffari/online-multiclass-lpboost">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Homotopy Regularization for Boosting (ICDM 2010)</strong></p> <ul dir="auto"> <li>Zheng Wang, Yangqiu Song, Changshui Zhang</li> <li><a href="https://ieeexplore.ieee.org/document/5694094" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Exploiting Local Data Uncertainty to Boost Global Outlier Detection (ICDM 2010)</strong></p> <ul dir="auto"> <li>Bo Liu, Jie Yin, Yanshan Xiao, Longbing Cao, Philip S. Yu</li> <li><a href="https://ieeexplore.ieee.org/document/5693984" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Classifiers with Tightened L0-Relaxation Penalties (ICML 2010)</strong></p> <ul dir="auto"> <li>Noam Goldberg, Jonathan Eckstein</li> <li><a href="https://pdfs.semanticscholar.org/11df/aed4ec2a2f72878789fa3a54d588d693bdda.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting for Regression Transfer (ICML 2010)</strong></p> <ul dir="auto"> <li>David Pardoe, Peter Stone</li> <li><a href="https://www.cs.utexas.edu/~dpardoe/papers/ICML10.pdf" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/jay15summer/Two-stage-TrAdaboost.R2">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Backpropagation Learning for Training Deep Modular Networks (ICML 2010)</strong></p> <ul dir="auto"> <li>Alexander Grubb, J. Andrew Bagnell</li> <li><a href="https://icml.cc/Conferences/2010/papers/451.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Fast Boosting Using Adversarial Bandits (ICML 2010)</strong></p> <ul dir="auto"> <li>Róbert Busa-Fekete, Balázs Kégl</li> <li><a href="https://www.lri.fr/~kegl/research/PDFs/BuKe10.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting with Structure Information in the Functional Space: an Application to Graph Classification (KDD 2010)</strong></p> <ul dir="auto"> <li>Hongliang Fei, Jun Huan</li> <li><a href="https://dl.acm.org/citation.cfm?id=1835804.1835886" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Multi-task Learning for Boosting with Application to Web Search Ranking (KDD 2010)</strong></p> <ul dir="auto"> <li>Olivier Chapelle, Pannagadatta K. Shivaswamy, Srinivas Vadrevu, Kilian Q. Weinberger, Ya Zhang, Belle L. Tseng</li> <li><a href="https://dl.acm.org/citation.cfm?id=1835953" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Theory of Multiclass Boosting (NIPS 2010)</strong></p> <ul dir="auto"> <li>Indraneel Mukherjee, Robert E. Schapire</li> <li><a href="http://rob.schapire.net/papers/multiboost-journal.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Classifier Cascades (NIPS 2010)</strong></p> <ul dir="auto"> <li>Mohammad J. Saberian, Nuno Vasconcelos</li> <li><a href="https://papers.nips.cc/paper/4033-boosting-classifier-cascades.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Joint Cascade Optimization Using A Product Of Boosted Classifiers (NIPS 2010)</strong></p> <ul dir="auto"> <li>Leonidas Lefakis, François Fleuret</li> <li><a href="https://papers.nips.cc/paper/4148-joint-cascade-optimization-using-a-product-of-boosted-classifiers" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost (UAI 2010)</strong></p> <ul dir="auto"> <li>Ping Li</li> <li><a href="https://arxiv.org/abs/1203.3491" rel="nofollow">[Paper]</a></li> <li><a href="https://github.com/pengsun/AOSOLogitBoost">[Code]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2009</h2><a id="user-content-2009" class="anchor" aria-label="Permalink: 2009" href="#2009"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Feature Selection for Ranking Using Boosted Trees (CIKM 2009)</strong></p> <ul dir="auto"> <li>Feng Pan, Tim Converse, David Ahn, Franco Salvetti, Gianluca Donato</li> <li><a href="http://www.francosalvetti.com/cikm09_camera2.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting KNN Text Classification Accuracy by Using Supervised Term Weighting Schemes (CIKM 2009)</strong></p> <ul dir="auto"> <li>Iyad Batal, Milos Hauskrecht</li> <li><a href="https://people.cs.pitt.edu/~milos/research/CIKM_2009_boosting_KNN.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Stochastic Gradient Boosted Distributed Decision Trees (CIKM 2009)</strong></p> <ul dir="auto"> <li>Jerry Ye, Jyh-Herng Chow, Jiang Chen, Zhaohui Zheng</li> <li><a href="http://cse.iitrpr.ac.in/ckn/courses/f2012/thomas.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A General Magnitude-Preserving Boosting Algorithm for Search Ranking (CIKM 2009)</strong></p> <ul dir="auto"> <li>Chenguang Zhu, Weizhu Chen, Zeyuan Allen Zhu, Gang Wang, Dong Wang, Zheng Chen</li> <li><a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/cikm2009-1.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Reducing Joint Boost-Based Multiclass Classification to Proximity Search (CVPR 2009)</strong></p> <ul dir="auto"> <li>Alexandra Stefan, Vassilis Athitsos, Quan Yuan, Stan Sclaroff</li> <li><a href="https://www.semanticscholar.org/paper/Reducing-JointBoost-based-multiclass-classification-Stefan-Athitsos/08ba1a7d91ce9b4ac26869bfe4bb7c955b0d1a24" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Imbalanced RankBoost for Efficiently Ranking Large-Scale Image-Video Collections (CVPR 2009)</strong></p> <ul dir="auto"> <li>Michele Merler, Rong Yan, John R. Smith</li> <li><a href="https://www.semanticscholar.org/paper/Imbalanced-RankBoost-for-efficiently-ranking-Merler-Yan/031ba6bf0d6df8bd3aa686ce85791b7d74f0b6d5" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Regularized Multi-Class Semi-Supervised Boosting (CVPR 2009)</strong></p> <ul dir="auto"> <li>Amir Saffari, Christian Leistner, Horst Bischof</li> <li><a href="https://ieeexplore.ieee.org/abstract/document/5206715" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene (CVPR 2009)</strong></p> <ul dir="auto"> <li>Yuan Li, Chang Huang, Ram Nevatia</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.8335&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Multi-task Learning for Face Verification with Applications to Web Image and Video Search (CVPR 2009)</strong></p> <ul dir="auto"> <li>Xiaogang Wang, Cha Zhang, Zhengyou Zhang</li> <li><a href="http://www.ee.cuhk.edu.hk/~xgwang/webface.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>LidarBoost: Depth Superresolution for ToF 3D Shape Scanning (CVPR 2009)</strong></p> <ul dir="auto"> <li>Sebastian Schuon, Christian Theobalt, James E. Davis, Sebastian Thrun</li> <li><a href="http://ai.stanford.edu/~schuon/sr/cvpr09_poster_lidarboost.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Model Adaptation via Model Interpolation and Boosting for Web Search Ranking (EMNLP 2009)</strong></p> <ul dir="auto"> <li>Jianfeng Gao, Qiang Wu, Chris Burges, Krysta Marie Svore, Yi Su, Nazan Khan, Shalin Shah, Hongyan Zhou</li> <li><a href="https://pdfs.semanticscholar.org/7a82/66335d0b44596574588eabb090bfeae4ab35.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Finding Shareable Informative Patterns and Optimal Coding Matrix for Multiclass Boosting (ICCV 2009)</strong></p> <ul dir="auto"> <li>Bang Zhang, Getian Ye, Yang Wang, Jie Xu, Gunawan Herman</li> <li><a href="https://ieeexplore.ieee.org/document/5459146" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>RankBoost with L1 Regularization for Facial Expression Recognition and Intensity Estimation (ICCV 2009)</strong></p> <ul dir="auto"> <li>Peng Yang, Qingshan Liu, Dimitris N. Metaxas</li> <li><a href="https://ieeexplore.ieee.org/document/5459371" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Robust Boosting Tracker with Minimum Error Bound in a Co-Training Framework (ICCV 2009)</strong></p> <ul dir="auto"> <li>Rong Liu, Jian Cheng, Hanqing Lu</li> <li><a href="http://nlpr-web.ia.ac.cn/2009papers/gjhy/gh1.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Tutorial Summary: Survey of Boosting from an Optimization Perspective (ICML 2009)</strong></p> <ul dir="auto"> <li>Manfred K. Warmuth, S. V. N. Vishwanathan</li> <li><a href="http://www.stat.purdue.edu/~vishy/erlpboost/manfred.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Products of Base Classifiers (ICML 2009)</strong></p> <ul dir="auto"> <li>Balázs Kégl, Róbert Busa-Fekete</li> <li><a href="https://users.lal.in2p3.fr/kegl/research/PDFs/keglBusafekete09.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>ABC-boost: Adaptive Base Class Boost for Multi-Class Classification (ICML 2009)</strong></p> <ul dir="auto"> <li>Ping Li</li> <li><a href="https://icml.cc/Conferences/2009/papers/417.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting with Structural Sparsity (ICML 2009)</strong></p> <ul dir="auto"> <li>John C. Duchi, Yoram Singer</li> <li><a href="https://web.stanford.edu/~jduchi/projects/DuchiSi09a.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Constrained Mutual Subspace Method for Robust Image-Set Based Object Recognition (IJCAI 2009)</strong></p> <ul dir="auto"> <li>Xi Li, Kazuhiro Fukui, Nanning Zheng</li> <li><a href="https://www.researchgate.net/publication/220812439_Boosting_Constrained_Mutual_Subspace_Method_for_Robust_Image-Set_Based_Object_Recognition" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Information Theoretic Regularization for Semi-supervised Boosting (KDD 2009)</strong></p> <ul dir="auto"> <li>Lei Zheng, Shaojun Wang, Yan Liu, Chi-Hoon Lee</li> <li><a href="https://pdfs.semanticscholar.org/5255/242d50851ce56354e10ae8fdcee6f47591c9.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Potential-Based Agnostic Boosting (NIPS 2009)</strong></p> <ul dir="auto"> <li>Adam Kalai, Varun Kanade</li> <li><a href="https://papers.nips.cc/paper/3676-potential-based-agnostic-boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Positive Semidefinite Metric Learning with Boosting (NIPS 2009)</strong></p> <ul dir="auto"> <li>Chunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel</li> <li><a href="https://papers.nips.cc/paper/3658-positive-semidefinite-metric-learning-with-boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting with Spatial Regularization (NIPS 2009)</strong></p> <ul dir="auto"> <li>Zhen James Xiang, Yongxin Taylor Xi, Uri Hasson, Peter J. Ramadge</li> <li><a href="https://papers.nips.cc/paper/3696-boosting-with-spatial-regularization" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Effective Boosting of Na%C3%AFve Bayesian Classifiers by Local Accuracy Estimation (PAKDD 2009)</strong></p> <ul dir="auto"> <li>Zhipeng Xie</li> <li><a href="https://link.springer.com/chapter/10.1007/978-3-642-01307-2_88" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Multi-resolution Boosting for Classification and Regression Problems (PAKDD 2009)</strong></p> <ul dir="auto"> <li>Chandan K. Reddy, Jin Hyeong Park</li> <li><a href="http://dmkd.cs.vt.edu/papers/PAKDD09.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Efficient Active Learning with Boosting (SDM 2009)</strong></p> <ul dir="auto"> <li>Zheng Wang, Yangqiu Song, Changshui Zhang</li> <li><a href="https://pdfs.semanticscholar.org/c8be/b70c37e4b4c4ad77e46b39060c977779d201.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2008</h2><a id="user-content-2008" class="anchor" aria-label="Permalink: 2008" href="#2008"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Group-Based Learning: A Boosting Approach (CIKM 2008)</strong></p> <ul dir="auto"> <li>Weijian Ni, Jun Xu, Hang Li, Yalou Huang</li> <li><a href="http://www.bigdatalab.ac.cn/~junxu/publications/CIKM2008_GroupLearn.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Semi-Supervised Boosting Using Visual Similarity Learning (CVPR 2008)</strong></p> <ul dir="auto"> <li>Christian Leistner, Helmut Grabner, Horst Bischof</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.144.7914&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Mining Compositional Features for Boosting (CVPR 2008)</strong></p> <ul dir="auto"> <li>Junsong Yuan, Jiebo Luo, Ying Wu</li> <li><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4587347" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Deformable Model for Human Body Alignment (CVPR 2008)</strong></p> <ul dir="auto"> <li>Xiaoming Liu, Ting Yu, Thomas Sebastian, Peter H. Tu</li> <li><a href="https://www.cse.msu.edu/~liuxm/publication/Liu_Yu_Sebastian_Tu_cvpr08.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Discriminative Modeling by Boosting on Multilevel Aggregates (CVPR 2008)</strong></p> <ul dir="auto"> <li>Jason J. Corso</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.3166&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Face Alignment via Boosted Ranking Model (CVPR 2008)</strong></p> <ul dir="auto"> <li>Hao Wu, Xiaoming Liu, Gianfranco Doretto</li> <li><a href="https://ieeexplore.ieee.org/document/4587753" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Adaptive Linear Weak Classifiers for Online Learning and Tracking (CVPR 2008)</strong></p> <ul dir="auto"> <li>Toufiq Parag, Fatih Porikli, Ahmed M. Elgammal</li> <li><a href="https://www.merl.com/publications/docs/TR2008-065.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Detection with Multi-Exit Asymmetric Boosting (CVPR 2008)</strong></p> <ul dir="auto"> <li>Minh-Tri Pham, V-D. D. Hoang, Tat-Jen Cham</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.330.6364&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Ordinal Features for Accurate and Fast Iris Recognition (CVPR 2008)</strong></p> <ul dir="auto"> <li>Zhaofeng He, Zhenan Sun, Tieniu Tan, Xianchao Qiu, Cheng Zhong, Wenbo Dong</li> <li><a href="https://www.researchgate.net/publication/224323296_Boosting_ordinal_features_for_accurate_and_fast_iris_recognition" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Adaptive and Compact Shape Descriptor by Progressive Feature Combination and Selection with Boosting (CVPR 2008)</strong></p> <ul dir="auto"> <li>Cheng Chen, Yueting Zhuang, Jun Xiao, Fei Wu</li> <li><a href="https://ieeexplore.ieee.org/document/4587613" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Relational Sequence Alignments (ICDM 2008)</strong></p> <ul dir="auto"> <li>Andreas Karwath, Kristian Kersting, Niels Landwehr</li> <li><a href="https://www.cs.uni-potsdam.de/~landwehr/ICDM08boosting.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting with Incomplete Information (ICML 2008)</strong></p> <ul dir="auto"> <li>Gholamreza Haffari, Yang Wang, Shaojun Wang, Greg Mori, Feng Jiao</li> <li><a href="http://users.monash.edu.au/~gholamrh/publications/boosting_icml08_slides.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>ManifoldBoost: Stagewise Function Approximation for Fully-, Semi- and Un-supervised Learning (ICML 2008)</strong></p> <ul dir="auto"> <li>Nicolas Loeff, David A. Forsyth, Deepak Ramachandran</li> <li><a href="http://reason.cs.uiuc.edu/deepak/manifoldboost.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Random Classification Noise Defeats All Convex Potential Boosters (ICML 2008)</strong></p> <ul dir="auto"> <li>Philip M. Long, Rocco A. Servedio</li> <li><a href="http://phillong.info/publications/LS09_potential.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Multi-class Cost-Sensitive Boosting with P-norm Loss Functions (KDD 2008)</strong></p> <ul dir="auto"> <li>Aurelie C. Lozano, Naoki Abe</li> <li><a href="https://dl.acm.org/citation.cfm?id=1401953" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features (NIPS 2008)</strong></p> <ul dir="auto"> <li>Tae-Kyun Kim, Roberto Cipolla</li> <li><a href="https://papers.nips.cc/paper/3483-mcboost-multiple-classifier-boosting-for-perceptual-co-clustering-of-images-and-visual-features" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning (NIPS 2008)</strong></p> <ul dir="auto"> <li>Chunhua Shen, Alan Welsh, Lei Wang</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.879.7750&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>On the Design of Loss Functions for Classification: Theory, Robustness to Outliers, and SavageBoost (NIPS 2008)</strong></p> <ul dir="auto"> <li>Hamed Masnadi-Shirazi, Nuno Vasconcelos</li> <li><a href="https://papers.nips.cc/paper/3591-on-the-design-of-loss-functions-for-classification-theory-robustness-to-outliers-and-savageboost" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Adaptive Martingale Boosting (NIPS 2008)</strong></p> <ul dir="auto"> <li>Philip M. Long, Rocco A. Servedio</li> <li><a href="http://phillong.info/publications/LS08_adaptive_martingale_boosting.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Boosting Algorithm for Learning Bipartite Ranking Functions with Partially Labeled Data (SIGIR 2008)</strong></p> <ul dir="auto"> <li>Massih-Reza Amini, Tuong-Vinh Truong, Cyril Goutte</li> <li><a href="http://ama.liglab.fr/~amini/Publis/SemiSupRanking_sigir08.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2007</h2><a id="user-content-2007" class="anchor" aria-label="Permalink: 2007" href="#2007"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Using Error-Correcting Output Codes with Model-Refinement to Boost Centroid Text Classifier (ACL 2007)</strong></p> <ul dir="auto"> <li>Songbo Tan</li> <li><a href="https://dl.acm.org/citation.cfm?id=1557794" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Fast Human Pose Estimation using Appearance and Motion via Multi-Dimensional Boosting Regression (CVPR 2007)</strong></p> <ul dir="auto"> <li>Alessandro Bissacco, Ming-Hsuan Yang, Stefano Soatto</li> <li><a href="http://vision.ucla.edu/papers/bissaccoYS07.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Generic Face Alignment using Boosted Appearance Model (CVPR 2007)</strong></p> <ul dir="auto"> <li>Xiaoming Liu</li> <li><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4270290" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Eigenboosting: Combining Discriminative and Generative Information (CVPR 2007)</strong></p> <ul dir="auto"> <li>Helmut Grabner, Peter M. Roth, Horst Bischof</li> <li><a href="https://www.tugraz.at/fileadmin/user_upload/Institute/ICG/Documents/lrs/pubs/grabner_cvpr_07.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Online Learning Asymmetric Boosted Classifiers for Object Detection (CVPR 2007)</strong></p> <ul dir="auto"> <li>Minh-Tri Pham, Tat-Jen Cham</li> <li><a href="https://ieeexplore.ieee.org/abstract/document/4270108" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Improving Part based Object Detection by Unsupervised Online Boosting (CVPR 2007)</strong></p> <ul dir="auto"> <li>Bo Wu, Ram Nevatia</li> <li><a href="https://ieeexplore.ieee.org/document/4270173" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Specialized Processor Suitable for AdaBoost-Based Detection with Haar-like Features (CVPR 2007)</strong></p> <ul dir="auto"> <li>Masayuki Hiromoto, Kentaro Nakahara, Hiroki Sugano, Yukihiro Nakamura, Ryusuke Miyamoto</li> <li><a href="https://ieeexplore.ieee.org/document/4270413" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Simultaneous Object Detection and Segmentation by Boosting Local Shape Feature based Classifier (CVPR 2007)</strong></p> <ul dir="auto"> <li>Bo Wu, Ram Nevatia</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.9795&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Compositional Boosting for Computing Hierarchical Image Structures (CVPR 2007)</strong></p> <ul dir="auto"> <li>Tianfu Wu, Gui-Song Xia, Song Chun Zhu</li> <li><a href="https://ieeexplore.ieee.org/document/4270059" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Coded Dynamic Features for Facial Action Units and Facial Expression Recognition (CVPR 2007)</strong></p> <ul dir="auto"> <li>Peng Yang, Qingshan Liu, Dimitris N. Metaxas</li> <li><a href="https://ieeexplore.ieee.org/document/4270084" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Object Classification in Visual Surveillance Using Adaboost (CVPR 2007)</strong></p> <ul dir="auto"> <li>John-Paul Renno, Dimitrios Makris, Graeme A. Jones</li> <li><a href="https://ieeexplore.ieee.org/abstract/document/4270512" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Boosting Regression Approach to Medical Anatomy Detection (CVPR 2007)</strong></p> <ul dir="auto"> <li>Shaohua Kevin Zhou, Jinghao Zhou, Dorin Comaniciu</li> <li><a href="http://ww.w.comaniciu.net/Papers/BoostingRegression_CVPR07.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Joint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network (CVPR 2007)</strong></p> <ul dir="auto"> <li>Jingdan Zhang, Shaohua Kevin Zhou, Leonard McMillan, Dorin Comaniciu</li> <li><a href="http://csbio.unc.edu/mcmillan/pubs/CVPR07_Zhang.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Kernel Sharing With Joint Boosting For Multi-Class Concept Detection (CVPR 2007)</strong></p> <ul dir="auto"> <li>Wei Jiang, Shih-Fu Chang, Alexander C. Loui</li> <li><a href="http://www.ee.columbia.edu/~wjiang/references/jiangcvprws07.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Scale-Space Based Weak Regressors for Boosting (ECML 2007)</strong></p> <ul dir="auto"> <li>Jin Hyeong Park, Chandan K. Reddy</li> <li><a href="http://www.cs.wayne.edu/~reddy/Papers/ECML07.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Avoiding Boosting Overfitting by Removing Confusing Samples (ECML 2007)</strong></p> <ul dir="auto"> <li>Alexander Vezhnevets, Olga Barinova</li> <li><a href="http://groups.inf.ed.ac.uk/calvin/hp_avezhnev/Pubs/AvoidingBoostingOverfitting.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>DynamicBoost: Boosting Time Series Generated by Dynamical Systems (ICCV 2007)</strong></p> <ul dir="auto"> <li>René Vidal, Paolo Favaro</li> <li><a href="http://vision.jhu.edu/assets/VidalICCV07.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Incremental Learning of Boosted Face Detector (ICCV 2007)</strong></p> <ul dir="auto"> <li>Chang Huang, Haizhou Ai, Takayoshi Yamashita, Shihong Lao, Masato Kawade</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.126.9012&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Gradient Feature Selection for Online Boosting (ICCV 2007)</strong></p> <ul dir="auto"> <li>Xiaoming Liu, Ting Yu</li> <li><a href="https://www.cse.msu.edu/~liuxm/publication/Liu_Yu_ICCV2007.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Fast Training and Selection of Haar Features Using Statistics in Boosting-based Face Detection (ICCV 2007)</strong></p> <ul dir="auto"> <li>Minh-Tri Pham, Tat-Jen Cham</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.212.6173&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Cluster Boosted Tree Classifier for Multi-View - Multi-Pose Object Detection (ICCV 2007)</strong></p> <ul dir="auto"> <li>Bo Wu, Ramakant Nevatia</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.9885&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Asymmetric Boosting (ICML 2007)</strong></p> <ul dir="auto"> <li>Hamed Masnadi-Shirazi, Nuno Vasconcelos</li> <li><a href="http://www.svcl.ucsd.edu/publications/conference/2007/icml07/AsymmetricBoosting.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting for Transfer Learning (ICML 2007)</strong></p> <ul dir="auto"> <li>Wenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu</li> <li><a href="http://www.cs.ust.hk/~qyang/Docs/2007/tradaboost.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Gradient Boosting for Kernelized Output Spaces (ICML 2007)</strong></p> <ul dir="auto"> <li>Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.435.3970&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting a Complete Technique to Find MSS and MUS Thanks to a Local Search Oracle (IJCAI 2007)</strong></p> <ul dir="auto"> <li>Éric Grégoire, Bertrand Mazure, Cédric Piette</li> <li><a href="http://www.cril.univ-artois.fr/~piette/IJCAI07_HYCAM.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Training Conditional Random Fields Using Virtual Evidence Boosting (IJCAI 2007)</strong></p> <ul dir="auto"> <li>Lin Liao, Tanzeem Choudhury, Dieter Fox, Henry A. Kautz</li> <li><a href="https://www.ijcai.org/Proceedings/07/Papers/407.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Simple Training of Dependency Parsers via Structured Boosting (IJCAI 2007)</strong></p> <ul dir="auto"> <li>Qin Iris Wang, Dekang Lin, Dale Schuurmans</li> <li><a href="https://www.ijcai.org/Proceedings/07/Papers/284.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Real Boosting a la Carte with an Application to Boosting Oblique Decision Tree (IJCAI 2007)</strong></p> <ul dir="auto"> <li>Claudia Henry, Richard Nock, Frank Nielsen</li> <li><a href="https://www.ijcai.org/Proceedings/07/Papers/135.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Managing Domain Knowledge and Multiple Models with Boosting (IJCAI 2007)</strong></p> <ul dir="auto"> <li>Peng Zang, Charles Lee Isbell Jr.</li> <li><a href="https://www.ijcai.org/Proceedings/07/Papers/185.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Model-Shared Subspace Boosting for Multi-label Classification (KDD 2007)</strong></p> <ul dir="auto"> <li>Rong Yan, Jelena Tesic, John R. Smith</li> <li><a href="http://rogerioferis.com/VisualRecognitionAndSearch2014/material/papers/IMARSKDD2007.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Regularized Boost for Semi-Supervised Learning (NIPS 2007)</strong></p> <ul dir="auto"> <li>Ke Chen, Shihai Wang</li> <li><a href="https://papers.nips.cc/paper/3167-regularized-boost-for-semi-supervised-learning.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Algorithms for Maximizing the Soft Margin (NIPS 2007)</strong></p> <ul dir="auto"> <li>Manfred K. Warmuth, Karen A. Glocer, Gunnar Rätsch</li> <li><a href="https://papers.nips.cc/paper/3374-boosting-algorithms-for-maximizing-the-soft-margin.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>McRank: Learning to Rank Using Multiple Classification and Gradient Boosting (NIPS 2007)</strong></p> <ul dir="auto"> <li>Ping Li, Christopher J. C. Burges, Qiang Wu</li> <li><a href="https://papers.nips.cc/paper/3270-mcrank-learning-to-rank-using-multiple-classification-and-gradient-boosting.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>One-Pass Boosting (NIPS 2007)</strong></p> <ul dir="auto"> <li>Zafer Barutçuoglu, Philip M. Long, Rocco A. Servedio</li> <li><a href="http://phillong.info/publications/BLS07_one_pass.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting the Area under the ROC Curve (NIPS 2007)</strong></p> <ul dir="auto"> <li>Philip M. Long, Rocco A. Servedio</li> <li><a href="https://papers.nips.cc/paper/3247-boosting-the-area-under-the-roc-curve.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>FilterBoost: Regression and Classification on Large Datasets (NIPS 2007)</strong></p> <ul dir="auto"> <li>Joseph K. Bradley, Robert E. Schapire</li> <li><a href="http://rob.schapire.net/papers/FilterBoost_paper.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A General Boosting Method and its Application to Learning Ranking Functions for Web Search (NIPS 2007)</strong></p> <ul dir="auto"> <li>Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun</li> <li><a href="https://pdfs.semanticscholar.org/8f8d/874a3f0217289ba317b1f6175ac3b6f73d70.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Efficient Multiclass Boosting Classification with Active Learning (SDM 2007)</strong></p> <ul dir="auto"> <li>Jian Huang, Seyda Ertekin, Yang Song, Hongyuan Zha, C. Lee Giles</li> <li><a href="https://epubs.siam.org/doi/abs/10.1137/1.9781611972771.27" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>AdaRank: a Boosting Algorithm for Information Retrieval (SIGIR 2007)</strong></p> <ul dir="auto"> <li>Jun Xu, Hang Li</li> <li><a href="http://www.bigdatalab.ac.cn/~junxu/publications/SIGIR2007_AdaRank.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2006</h2><a id="user-content-2006" class="anchor" aria-label="Permalink: 2006" href="#2006"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Gradient Boosting for Sequence Alignment (AAAI 2006)</strong></p> <ul dir="auto"> <li>Charles Parker, Alan Fern, Prasad Tadepalli</li> <li><a href="http://web.engr.oregonstate.edu/~afern/papers/aaai06-align.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Kernel Models for Regression (ICDM 2006)</strong></p> <ul dir="auto"> <li>Ping Sun, Xin Yao</li> <li><a href="https://www.cs.bham.ac.uk/~xin/papers/icdm06SunYao.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting for Learning Multiple Classes with Imbalanced Class Distribution (ICDM 2006)</strong></p> <ul dir="auto"> <li>Yanmin Sun, Mohamed S. Kamel, Yang Wang</li> <li><a href="http://people.ee.duke.edu/~lcarin/ImbalancedClassDistribution.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting the Feature Space: Text Classification for Unstructured Data on the Web (ICDM 2006)</strong></p> <ul dir="auto"> <li>Yang Song, Ding Zhou, Jian Huang, Isaac G. Councill, Hongyuan Zha, C. Lee Giles</li> <li><a href="http://sonyis.me/paperpdf/icdm06_song.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Totally Corrective Boosting Algorithms that Maximize the Margin (ICML 2006)</strong></p> <ul dir="auto"> <li>Manfred K. Warmuth, Jun Liao, Gunnar Rätsch</li> <li><a href="https://users.soe.ucsc.edu/~manfred/pubs/C75.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>How Boosting the Margin Can Also Boost Classifier Complexity (ICML 2006)</strong></p> <ul dir="auto"> <li>Lev Reyzin, Robert E. Schapire</li> <li><a href="http://rob.schapire.net/papers/boost_complexity.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Multiclass Boosting with Repartitioning (ICML 2006)</strong></p> <ul dir="auto"> <li>Ling Li</li> <li><a href="https://authors.library.caltech.edu/72259/1/p569-li.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>AdaBoost is Consistent (NIPS 2006)</strong></p> <ul dir="auto"> <li>Peter L. Bartlett, Mikhail Traskin</li> <li><a href="http://jmlr.csail.mit.edu/papers/volume8/bartlett07b/bartlett07b.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Structured Prediction for Imitation Learning (NIPS 2006)</strong></p> <ul dir="auto"> <li>Nathan D. Ratliff, David M. Bradley, J. Andrew Bagnell, Joel E. Chestnutt</li> <li><a href="https://papers.nips.cc/paper/3154-boosting-structured-prediction-for-imitation-learning.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Chained Boosting (NIPS 2006)</strong></p> <ul dir="auto"> <li>Christian R. Shelton, Wesley Huie, Kin Fai Kan</li> <li><a href="https://papers.nips.cc/paper/2981-chained-boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>When Efficient Model Averaging Out-Performs Boosting and Bagging (PKDD 2006)</strong></p> <ul dir="auto"> <li>Ian Davidson, Wei Fan</li> <li><a href="https://link.springer.com/chapter/10.1007/11871637_46" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2005</h2><a id="user-content-2005" class="anchor" aria-label="Permalink: 2005" href="#2005"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Semantic Place Classification of Indoor Environments with Mobile Robots Using Boosting (AAAI 2005)</strong></p> <ul dir="auto"> <li>Axel Rottmann, Óscar Martínez Mozos, Cyrill Stachniss, Wolfram Burgard</li> <li><a href="http://www2.informatik.uni-freiburg.de/~stachnis/pdf/rottmann05aaai.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting-based Parse Reranking with Subtree Features (ACL 2005)</strong></p> <ul dir="auto"> <li>Taku Kudo, Jun Suzuki, Hideki Isozaki</li> <li><a href="http://chasen.org/~taku/publications/acl2005.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Using RankBoost to Compare Retrieval Systems (CIKM 2005)</strong></p> <ul dir="auto"> <li>Huyen-Trang Vu, Patrick Gallinari</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.98.9470&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Classifier Fusion Using Shared Sampling Distribution for Boosting (ICDM 2005)</strong></p> <ul dir="auto"> <li>Costin Barbu, Raja Tanveer Iqbal, Jing Peng</li> <li><a href="https://ieeexplore.ieee.org/document/1565659" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Semi-Supervised Mixture of Kernels via LPBoost Methods (ICDM 2005)</strong></p> <ul dir="auto"> <li>Jinbo Bi, Glenn Fung, Murat Dundar, R. Bharat Rao</li> <li><a href="https://ieeexplore.ieee.org/document/1565728" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Efficient Discriminative Learning of Bayesian Network Classifier via Boosted Augmented Naive Bayes (ICML 2005)</strong></p> <ul dir="auto"> <li>Yushi Jing, Vladimir Pavlovic, James M. Rehg</li> <li><a href="http://mrl.isr.uc.pt/pub/bscw.cgi/d27355/Jing05Efficient.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Unifying the Error-Correcting and Output-Code AdaBoost within the Margin Framework (ICML 2005)</strong></p> <ul dir="auto"> <li>Yijun Sun, Sinisa Todorovic, Jian Li, Dapeng Wu</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.138.4246&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Smoothed Boosting Algorithm Using Probabilistic Output Codes (ICML 2005)</strong></p> <ul dir="auto"> <li>Rong Jin, Jian Zhang</li> <li><a href="http://www.stat.purdue.edu/~jianzhan/papers/icml05jin.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Robust Boosting and its Relation to Bagging (KDD 2005)</strong></p> <ul dir="auto"> <li>Saharon Rosset</li> <li><a href="https://www.tau.ac.il/~saharon/papers/bagboost.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Efficient Computations via Scalable Sparse Kernel Partial Least Squares and Boosted Latent Features (KDD 2005)</strong></p> <ul dir="auto"> <li>Michinari Momma</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.387.2078&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Multiple Instance Boosting for Object Detection (NIPS 2005)</strong></p> <ul dir="auto"> <li>Paul A. Viola, John C. Platt, Cha Zhang</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.138.8312&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations (NIPS 2005)</strong></p> <ul dir="auto"> <li>Aurelie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire</li> <li><a href="https://www.cs.princeton.edu/~schapire/papers/betamix.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted decision trees for word recognition in handwritten document retrieval (SIGIR 2005)</strong></p> <ul dir="auto"> <li>Nicholas R. Howe, Toni M. Rath, R. Manmatha</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.152.1551&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Obtaining Calibrated Probabilities from Boosting (UAI 2005)</strong></p> <ul dir="auto"> <li>Alexandru Niculescu-Mizil, Rich Caruana</li> <li><a href="https://www.cs.cornell.edu/~caruana/niculescu.scldbst.crc.rev4.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2004</h2><a id="user-content-2004" class="anchor" aria-label="Permalink: 2004" href="#2004"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Online Parallel Boosting (AAAI 2004)</strong></p> <ul dir="auto"> <li>Jesse A. Reichler, Harlan D. Harris, Michael A. Savchenko</li> <li><a href="https://www.aaai.org/Papers/AAAI/2004/AAAI04-059.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Boosting Approach to Multiple Instance Learning (ECML 2004)</strong></p> <ul dir="auto"> <li>Peter Auer, Ronald Ortner</li> <li><a href="https://link.springer.com/chapter/10.1007/978-3-540-30115-8_9" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Boosting Algorithm for Classification of Semi-Structured Text (EMNLP 2004)</strong></p> <ul dir="auto"> <li>Taku Kudo, Yuji Matsumoto</li> <li><a href="https://www.aclweb.org/anthology/W04-3239" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Text Classification by Boosting Weak Learners based on Terms and Concepts (ICDM 2004)</strong></p> <ul dir="auto"> <li>Stephan Bloehdorn, Andreas Hotho</li> <li><a href="https://ieeexplore.ieee.org/document/1410303" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Grammatical Inference with Confidence Oracles (ICML 2004)</strong></p> <ul dir="auto"> <li>Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier</li> <li><a href="http://www1.univ-ag.fr/~rnock/Articles/Drafts/icml04-jnss.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Surrogate Maximization/Minimization Algorithms for AdaBoost and the Logistic Regression Model (ICML 2004)</strong></p> <ul dir="auto"> <li>Zhihua Zhang, James T. Kwok, Dit-Yan Yeung</li> <li><a href="https://icml.cc/Conferences/2004/proceedings/papers/77.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Training Conditional Random Fields via Gradient Tree Boosting (ICML 2004)</strong></p> <ul dir="auto"> <li>Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov</li> <li><a href="http://web.engr.oregonstate.edu/~tgd/publications/ml2004-treecrf.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Margin Based Distance Functions for Clustering (ICML 2004)</strong></p> <ul dir="auto"> <li>Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall</li> <li><a href="http://www.cs.huji.ac.il/~daphna/papers/distboost-icml.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Column-Generation Boosting Methods for Mixture of Kernels (KDD 2004)</strong></p> <ul dir="auto"> <li>Jinbo Bi, Tong Zhang, Kristin P. Bennett</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.6359&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging (NIPS 2004)</strong></p> <ul dir="auto"> <li>Vladimir Koltchinskii, Manel Martínez-Ramón, Stefan Posse</li> <li><a href="https://papers.nips.cc/paper/2699-optimal-aggregation-of-classifiers-and-boosting-maps-in-functional-magnetic-resonance-imaging.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting on Manifolds: Adaptive Regularization of Base Classifiers (NIPS 2004)</strong></p> <ul dir="auto"> <li>Balázs Kégl, Ligen Wang</li> <li><a href="https://papers.nips.cc/paper/2613-boosting-on-manifolds-adaptive-regularization-of-base-classifiers" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Contextual Models for Object Detection Using Boosted Random Fields (NIPS 2004)</strong></p> <ul dir="auto"> <li>Antonio Torralba, Kevin P. Murphy, William T. Freeman</li> <li><a href="https://www.cs.ubc.ca/~murphyk/Papers/BRF-nips04-camera.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Generalization Error and Algorithmic Convergence of Median Boosting (NIPS 2004)</strong></p> <ul dir="auto"> <li>Balázs Kégl</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.70.8990&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>An Application of Boosting to Graph Classification (NIPS 2004)</strong></p> <ul dir="auto"> <li>Taku Kudo, Eisaku Maeda, Yuji Matsumoto</li> <li><a href="https://papers.nips.cc/paper/2739-an-application-of-boosting-to-graph-classification" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Logistic Regression and Boosting for Labeled Bags of Instances (PAKDD 2004)</strong></p> <ul dir="auto"> <li>Xin Xu, Eibe Frank</li> <li><a href="https://www.cs.waikato.ac.nz/~ml/publications/2004/xu-frank.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Fast and Light Boosting for Adaptive Mining of Data Streams (PAKDD 2004)</strong></p> <ul dir="auto"> <li>Fang Chu, Carlo Zaniolo</li> <li><a href="http://web.cs.ucla.edu/~zaniolo/papers/NBCAJMW77MW0J8CP.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2003</h2><a id="user-content-2003" class="anchor" aria-label="Permalink: 2003" href="#2003"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>On Boosting and the Exponential Loss (AISTATS 2003)</strong></p> <ul dir="auto"> <li>Abraham J. Wyner</li> <li><a href="http://www-stat.wharton.upenn.edu/~ajw/exploss.ps" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Support Vector Machines for Text Classification through Parameter-Free Threshold Relaxation (CIKM 2003)</strong></p> <ul dir="auto"> <li>James G. Shanahan, Norbert Roma</li> <li><a href="https://dl.acm.org/citation.cfm?id=956911" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Learning Cross-Document Structural Relationships Using Boosting (CIKM 2003)</strong></p> <ul dir="auto"> <li>Zhu Zhang, Jahna Otterbacher, Dragomir R. Radev</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.128.7712&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>On Boosting Improvement: Error Reduction and Convergence Speed-Up (ECML 2003)</strong></p> <ul dir="auto"> <li>Marc Sebban, Henri-Maxime Suchier</li> <li><a href="https://link.springer.com/chapter/10.1007/978-3-540-39857-8_32" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Lazy Decision Trees (ICML 2003)</strong></p> <ul dir="auto"> <li>Xiaoli Zhang Fern, Carla E. Brodley</li> <li><a href="https://www.aaai.org/Papers/ICML/2003/ICML03-026.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>On the Convergence of Boosting Procedures (ICML 2003)</strong></p> <ul dir="auto"> <li>Tong Zhang, Bin Yu</li> <li><a href="https://pdfs.semanticscholar.org/dd3f/901b232280533fbdb9e57f144f44723617cf.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Linear Programming Boosting for Uneven Datasets (ICML 2003)</strong></p> <ul dir="auto"> <li>Jure Leskovec, John Shawe-Taylor</li> <li><a href="https://cs.stanford.edu/people/jure/pubs/textbooster-icml03.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Monte Carlo Theory as an Explanation of Bagging and Boosting (IJCAI 2003)</strong></p> <ul dir="auto"> <li>Roberto Esposito, Lorenza Saitta</li> <li><a href="https://dl.acm.org/citation.cfm?id=1630733" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>On the Dynamics of Boosting (NIPS 2003)</strong></p> <ul dir="auto"> <li>Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire</li> <li><a href="https://papers.nips.cc/paper/2535-on-the-dynamics-of-boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Mutual Boosting for Contextual Inference (NIPS 2003)</strong></p> <ul dir="auto"> <li>Michael Fink, Pietro Perona</li> <li><a href="https://papers.nips.cc/paper/2520-mutual-boosting-for-contextual-inference" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Versus Covering (NIPS 2003)</strong></p> <ul dir="auto"> <li>Kohei Hatano, Manfred K. Warmuth</li> <li><a href="https://papers.nips.cc/paper/2532-boosting-versus-covering" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Multiple-Instance Learning via Disjunctive Programming Boosting (NIPS 2003)</strong></p> <ul dir="auto"> <li>Stuart Andrews, Thomas Hofmann</li> <li><a href="https://papers.nips.cc/paper/2478-multiple-instance-learning-via-disjunctive-programming-boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Averaged Boosting: A Noise-Robust Ensemble Method (PAKDD 2003)</strong></p> <ul dir="auto"> <li>Yongdai Kim</li> <li><a href="https://link.springer.com/chapter/10.1007/3-540-36175-8_38" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>SMOTEBoost: Improving Prediction of the Minority Class in Boosting (PKDD 2003)</strong></p> <ul dir="auto"> <li>Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, Kevin W. Bowyer</li> <li><a href="https://www3.nd.edu/~nchawla/papers/ECML03.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2002</h2><a id="user-content-2002" class="anchor" aria-label="Permalink: 2002" href="#2002"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Minimum Majority Classification and Boosting (AAAI 2002)</strong></p> <ul dir="auto"> <li>Philip M. Long</li> <li><a href="http://phillong.info/publications/minmaj.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Ranking Algorithms for Named Entity Extraction: Boosting and the Voted Perceptron (ACL 2002)</strong></p> <ul dir="auto"> <li>Michael Collins</li> <li><a href="https://www.aclweb.org/anthology/P02-1062" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting to Correct Inductive Bias in Text Classification (CIKM 2002)</strong></p> <ul dir="auto"> <li>Yan Liu, Yiming Yang, Jaime G. Carbonell</li> <li><a href="https://dl.acm.org/citation.cfm?id=584792.584850" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code (ECML 2002)</strong></p> <ul dir="auto"> <li>Günther Eibl, Karl Peter Pfeiffer</li> <li><a href="https://dl.acm.org/citation.cfm?id=650068" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Scaling Boosting by Margin-Based Inclusionof Features and Relations (ECML 2002)</strong></p> <ul dir="auto"> <li>Susanne Hoche, Stefan Wrobel</li> <li><a href="https://link.springer.com/chapter/10.1007/3-540-36755-1_13" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Robust Boosting Algorithm (ECML 2002)</strong></p> <ul dir="auto"> <li>Richard Nock, Patrice Lefaucheur</li> <li><a href="https://dl.acm.org/citation.cfm?id=650081" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>iBoost: Boosting Using an instance-Based Exponential Weighting Scheme (ECML 2002)</strong></p> <ul dir="auto"> <li>Stephen Kwek, Chau Nguyen</li> <li><a href="https://www.researchgate.net/publication/220516082_iBoost_Boosting_using_an_instance-based_exponential_weighting_scheme" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Density Function Estimators (ECML 2002)</strong></p> <ul dir="auto"> <li>Franck Thollard, Marc Sebban, Philippe Ézéquel</li> <li><a href="https://link.springer.com/chapter/10.1007%2F3-540-36755-1_36" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Statistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond (ICML 2002)</strong></p> <ul dir="auto"> <li>Tong Zhang</li> <li><a href="https://www.researchgate.net/publication/221344927_Statistical_Behavior_and_Consistency_of_Support_Vector_Machines_Boosting_and_Beyond" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Boosted Maximum Entropy Model for Learning Text Chunking (ICML 2002)</strong></p> <ul dir="auto"> <li>Seong-Bae Park, Byoung-Tak Zhang</li> <li><a href="https://www.researchgate.net/publication/221345636_A_Boosted_Maximum_Entropy_Model_for_Learning_Text_Chunking" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Towards Large Margin Speech Recognizers by Boosting and Discriminative Training (ICML 2002)</strong></p> <ul dir="auto"> <li>Carsten Meyer, Peter Beyerlein</li> <li><a href="https://www.semanticscholar.org/paper/Towards-Large-Margin-Speech-Recognizers-by-Boosting-Meyer-Beyerlein/8408479e36da812cdbf6bc15f7849c3e76a1016d" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Incorporating Prior Knowledge into Boosting (ICML 2002)</strong></p> <ul dir="auto"> <li>Robert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra K. Gupta</li> <li><a href="http://rob.schapire.net/papers/boostknowledge.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation (ICML 2002)</strong></p> <ul dir="auto"> <li>Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik</li> <li><a href="http://www.cs.utexas.edu/~ai-lab/pubs/ICML02-tac.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>MARK: A Boosting Algorithm for Heterogeneous Kernel Models (KDD 2002)</strong></p> <ul dir="auto"> <li>Kristin P. Bennett, Michinari Momma, Mark J. Embrechts</li> <li><a href="http://homepages.rpiscrews.us/~bennek/papers/kdd2.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Predicting rare classes: can boosting make any weak learner strong (KDD 2002)</strong></p> <ul dir="auto"> <li>Mahesh V. Joshi, Ramesh C. Agarwal, Vipin Kumar</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.13.1159&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Kernel Design Using Boosting (NIPS 2002)</strong></p> <ul dir="auto"> <li>Koby Crammer, Joseph Keshet, Yoram Singer</li> <li><a href="https://pdfs.semanticscholar.org/ff79/344807e972fdd7e5e1c3ed5c539dd1aeecbe.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>FloatBoost Learning for Classification (NIPS 2002)</strong></p> <ul dir="auto"> <li>Stan Z. Li, ZhenQiu Zhang, Heung-Yeung Shum, HongJiang Zhang</li> <li><a href="https://pdfs.semanticscholar.org/8ccc/5ef87eab96a4cae226750eba8322b30606ea.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Discriminative Learning for Label Sequences via Boosting (NIPS 2002)</strong></p> <ul dir="auto"> <li>Yasemin Altun, Thomas Hofmann, Mark Johnson</li> <li><a href="http://web.science.mq.edu.au/~mjohnson/papers/nips02.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Density Estimation (NIPS 2002)</strong></p> <ul dir="auto"> <li>Saharon Rosset, Eran Segal</li> <li><a href="https://papers.nips.cc/paper/2298-boosting-density-estimation.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Self Supervised Boosting (NIPS 2002)</strong></p> <ul dir="auto"> <li>Max Welling, Richard S. Zemel, Geoffrey E. Hinton</li> <li><a href="https://pdfs.semanticscholar.org/6a2a/f112a803e70c23b7055de2e73007cf42c301.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosted Dyadic Kernel Discriminants (NIPS 2002)</strong></p> <ul dir="auto"> <li>Baback Moghaddam, Gregory Shakhnarovich</li> <li><a href="http://www.merl.com/publications/docs/TR2002-55.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Method to Boost Support Vector Machines (PAKDD 2002)</strong></p> <ul dir="auto"> <li>Lili Diao, Keyun Hu, Yuchang Lu, Chunyi Shi</li> <li><a href="https://elkingarcia.github.io/Papers/MLDM07.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Method to Boost Naive Bayesian Classifiers (PAKDD 2002)</strong></p> <ul dir="auto"> <li>Lili Diao, Keyun Hu, Yuchang Lu, Chunyi Shi</li> <li><a href="https://link.springer.com/chapter/10.1007/3-540-47887-6_11" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting (PKDD 2002)</strong></p> <ul dir="auto"> <li>Mahesh V. Joshi, Ramesh C. Agarwal, Vipin Kumar</li> <li><a href="https://link.springer.com/chapter/10.1007/3-540-45681-3_20" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance (PKDD 2002)</strong></p> <ul dir="auto"> <li>Yuta Choki, Einoshin Suzuki</li> <li><a href="https://link.springer.com/chapter/10.1007/3-540-45681-3_8" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Staged Mixture Modelling and Boosting (UAI 2002)</strong></p> <ul dir="auto"> <li>Christopher Meek, Bo Thiesson, David Heckerman</li> <li><a href="https://arxiv.org/abs/1301.0586" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Advances in Boosting (UAI 2002)</strong></p> <ul dir="auto"> <li>Robert E. Schapire</li> <li><a href="http://rob.schapire.net/papers/uai02.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2001</h2><a id="user-content-2001" class="anchor" aria-label="Permalink: 2001" href="#2001"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Is Regularization Unnecessary for Boosting? (AISTATS 2001)</strong></p> <ul dir="auto"> <li>Wenxin Jiang</li> <li><a href="https://www.researchgate.net/publication/2439718_Is_Regularization_Unnecessary_for_Boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Online Bagging and Boosting (AISTATS 2001)</strong></p> <ul dir="auto"> <li>Nikunj C. Oza, Stuart J. Russell</li> <li><a href="https://ti.arc.nasa.gov/m/profile/oza/files/ozru01a.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Text Categorization Using Transductive Boosting (ECML 2001)</strong></p> <ul dir="auto"> <li>Hirotoshi Taira, Masahiko Haruno</li> <li><a href="https://link.springer.com/chapter/10.1007/3-540-44795-4_39" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Improving Term Extraction by System Combination Using Boosting (ECML 2001)</strong></p> <ul dir="auto"> <li>Jordi Vivaldi, Lluís Màrquez, Horacio Rodríguez</li> <li><a href="https://dl.acm.org/citation.cfm?id=3108351" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example (ECML 2001)</strong></p> <ul dir="auto"> <li>Günther Eibl, Karl Peter Pfeiffer</li> <li><a href="https://link.springer.com/chapter/10.1007/3-540-44795-4_10" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>On the Practice of Branching Program Boosting (ECML 2001)</strong></p> <ul dir="auto"> <li>Tapio Elomaa, Matti Kääriäinen</li> <li><a href="https://www.researchgate.net/publication/221112522_On_the_Practice_of_Branching_Program_Boosting" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Mixture Models for Semi-supervised Learning (ICANN 2001)</strong></p> <ul dir="auto"> <li>Yves Grandvalet, Florence d'Alché-Buc, Christophe Ambroise</li> <li>[[Paper]](<a href="https://link.springer.com/chapter/10.1007/3-540-44668-0_7" rel="nofollow">https://link.springer.com/chapter/10.1007/3-540-44668-0_7</a></li> </ul> </li> <li> <p dir="auto"><strong>A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods (ICDM 2001)</strong></p> <ul dir="auto"> <li>Bernard Zenko, Ljupco Todorovski, Saso Dzeroski</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.23.3118&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Using Boosting to Simplify Classification Models (ICDM 2001)</strong></p> <ul dir="auto"> <li>Virginia Wheway</li> <li><a href="https://ieeexplore.ieee.org/abstract/document/989565" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements (ICDM 2001)</strong></p> <ul dir="auto"> <li>Mahesh V. Joshi, Vipin Kumar, Ramesh C. Agarwal</li> <li><a href="https://pdfs.semanticscholar.org/b829/fe743e4beeeed65d32d2d7931354df7a2f60.pdf" rel="nofollow">[Paper]</a></li> <li><a href="/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master">[Code]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Neighborhood-Based Classifiers (ICML 2001)</strong></p> <ul dir="auto"> <li>Marc Sebban, Richard Nock, Stéphane Lallich</li> <li><a href="https://www.semanticscholar.org/paper/Boosting-Neighborhood-Based-Classifiers-Sebban-Nock/ee88e3bbe8a7e81cae7ee53da2c824de7c82f882" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Noisy Data (ICML 2001)</strong></p> <ul dir="auto"> <li>Abba Krieger, Chuan Long, Abraham J. Wyner</li> <li><a href="https://www.researchgate.net/profile/Abba_Krieger/publication/221345435_Boosting_Noisy_Data/links/00463528a1ba641692000000.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Some Theoretical Aspects of Boosting in the Presence of Noisy Data (ICML 2001)</strong></p> <ul dir="auto"> <li>Wenxin Jiang</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=2494A2C06ACA22FA971AC1C29B53FF62?doi=10.1.1.27.7231&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection (ICML 2001)</strong></p> <ul dir="auto"> <li>Sanmay Das</li> <li><a href="https://pdfs.semanticscholar.org/93b6/25a0e35b59fa6a3e7dc1cbdb31268d62d69f.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>The Distributed Boosting Algorithm (KDD 2001)</strong></p> <ul dir="auto"> <li>Aleksandar Lazarevic, Zoran Obradovic</li> <li><a href="https://www.researchgate.net/publication/2488971_The_Distributed_Boosting_Algorithm" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Experimental Comparisons of Online and Batch Versions of Bagging and Boosting (KDD 2001)</strong></p> <ul dir="auto"> <li>Nikunj C. Oza, Stuart J. Russell</li> <li><a href="https://people.eecs.berkeley.edu/~russell/papers/kdd01-online.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Semi-supervised MarginBoost (NIPS 2001)</strong></p> <ul dir="auto"> <li>Florence d'Alché-Buc, Yves Grandvalet, Christophe Ambroise</li> <li><a href="https://pdfs.semanticscholar.org/2197/f1c2d55827b6928cc80030922569acce2d6c.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting and Maximum Likelihood for Exponential Models (NIPS 2001)</strong></p> <ul dir="auto"> <li>Guy Lebanon, John D. Lafferty</li> <li><a href="https://papers.nips.cc/paper/2042-boosting-and-maximum-likelihood-for-exponential-models.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade (NIPS 2001)</strong></p> <ul dir="auto"> <li>Paul A. Viola, Michael J. Jones</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.68.4306&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Localized Classifiers in Heterogeneous Databases (SDM 2001)</strong></p> <ul dir="auto"> <li>Aleksandar Lazarevic, Zoran Obradovic</li> <li><a href="https://epubs.siam.org/doi/abs/10.1137/1.9781611972719.14" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2000</h2><a id="user-content-2000" class="anchor" aria-label="Permalink: 2000" href="#2000"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Boosted Wrapper Induction (AAAI 2000)</strong></p> <ul dir="auto"> <li>Dayne Freitag, Nicholas Kushmerick</li> <li><a href="https://pdfs.semanticscholar.org/d009/a2bd48a9d1971fbc0d99f6df00539a62048a.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>An Improved Boosting Algorithm and its Application to Text Categorization (CIKM 2000)</strong></p> <ul dir="auto"> <li>Fabrizio Sebastiani, Alessandro Sperduti, Nicola Valdambrini</li> <li><a href="http://nmis.isti.cnr.it/sebastiani/Publications/CIKM00.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting for Document Routing (CIKM 2000)</strong></p> <ul dir="auto"> <li>Raj D. Iyer, David D. Lewis, Robert E. Schapire, Yoram Singer, Amit Singhal</li> <li><a href="http://singhal.info/cikm-2000.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>On the Boosting Pruning Problem (ECML 2000)</strong></p> <ul dir="auto"> <li>Christino Tamon, Jie Xiang</li> <li><a href="https://link.springer.com/chapter/10.1007/3-540-45164-1_41" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Applied to Word Sense Disambiguation (ECML 2000)</strong></p> <ul dir="auto"> <li>Gerard Escudero, Lluís Màrquez, German Rigau</li> <li><a href="https://dl.acm.org/citation.cfm?id=649539" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>An Empirical Study of MetaCost Using Boosting Algorithms (ECML 2000)</strong></p> <ul dir="auto"> <li>Kai Ming Ting</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.218.1624&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness (ICML 2000)</strong></p> <ul dir="auto"> <li>Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum</li> <li><a href="https://www.researchgate.net/publication/221345746_FeatureBoost_A_Meta-Learning_Algorithm_that_Improves_Model_Robustness" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Comparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of Discourse (ICML 2000)</strong></p> <ul dir="auto"> <li>Tadashi Nomoto, Yuji Matsumoto</li> <li><a href="https://www.researchgate.net/publication/221344998_Comparing_the_Minimum_Description_Length_Principle_and_Boosting_in_the_Automatic_Analysis_of_Discourse" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Boosting Approach to Topic Spotting on Subdialogues (ICML 2000)</strong></p> <ul dir="auto"> <li>Kary Myers, Michael J. Kearns, Satinder P. Singh, Marilyn A. Walker</li> <li><a href="https://www.cis.upenn.edu/~mkearns/papers/topicspot.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Comparative Study of Cost-Sensitive Boosting Algorithms (ICML 2000)</strong></p> <ul dir="auto"> <li>Kai Ming Ting</li> <li><a href="https://dl.acm.org/citation.cfm?id=657944" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting a Positive-Data-Only Learner (ICML 2000)</strong></p> <ul dir="auto"> <li>Andrew R. Mitchell</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.3669" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Column Generation Algorithm For Boosting (ICML 2000)</strong></p> <ul dir="auto"> <li>Kristin P. Bennett, Ayhan Demiriz, John Shawe-Taylor</li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=1828D5853F656BD6892E9C2C446ECC68?doi=10.1.1.16.9612&rep=rep1&type=pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>A Gradient-Based Boosting Algorithm for Regression Problems (NIPS 2000)</strong></p> <ul dir="auto"> <li>Richard S. Zemel, Toniann Pitassi</li> <li><a href="https://pdfs.semanticscholar.org/c41a/9417f5605b55bdd216d119e47669a92f5c50.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Weak Learners and Improved Rates of Convergence in Boosting (NIPS 2000)</strong></p> <ul dir="auto"> <li>Shie Mannor, Ron Meir</li> <li><a href="https://papers.nips.cc/paper/1906-weak-learners-and-improved-rates-of-convergence-in-boosting.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Adaptive Boosting for Spatial Functions with Unstable Driving Attributes (PAKDD 2000)</strong></p> <ul dir="auto"> <li>Aleksandar Lazarevic, Tim Fiez, Zoran Obradovic</li> <li><a href="http://www.dabi.temple.edu/~zoran/papers/lazarevic01j.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Scaling Up a Boosting-Based Learner via Adaptive Sampling (PAKDD 2000)</strong></p> <ul dir="auto"> <li>Carlos Domingo, Osamu Watanabe</li> <li><a href="https://link.springer.com/chapter/10.1007/3-540-45571-X_37" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Learning First Order Logic Time Series Classifiers: Rules and Boosting (PKDD 2000)</strong></p> <ul dir="auto"> <li>Juan J. Rodríguez Diez, Carlos Alonso González, Henrik Boström</li> <li><a href="https://people.dsv.su.se/~henke/papers/rodriguez00b.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Bagging and Boosting with Dynamic Integration of Classifiers (PKDD 2000)</strong></p> <ul dir="auto"> <li>Alexey Tsymbal, Seppo Puuronen</li> <li><a href="https://link.springer.com/chapter/10.1007/3-540-45372-5_12" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Text Filtering by Boosting Naive Bayes Classifiers (SIGIR 2000)</strong></p> <ul dir="auto"> <li>Yu-Hwan Kim, Shang-Yoon Hahn, Byoung-Tak Zhang</li> <li><a href="https://www.researchgate.net/publication/221299823_Text_filtering_by_boosting_Naive_Bayes_classifiers" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">1999</h2><a id="user-content-1999" class="anchor" aria-label="Permalink: 1999" href="#1999"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Boosting Methodology for Regression Problems (AISTATS 1999)</strong></p> <ul dir="auto"> <li>Greg Ridgeway, David Madigan, Thomas Richardson</li> <li><a href="https://pdfs.semanticscholar.org/5f19/6a8baa281b2190c4519305bec8f5c91c8e5a.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Applied to Tagging and PP Attachment (EMNLP 1999)</strong></p> <ul dir="auto"> <li>Steven Abney, Robert E. Schapire, Yoram Singer</li> <li><a href="https://www.aclweb.org/anthology/W99-0606" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ICML 1999)</strong></p> <ul dir="auto"> <li>Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting</li> <li><a href="https://pdfs.semanticscholar.org/067e/86836ddbcb5e2844e955c16e058366a18c77.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>AdaCost: Misclassification Cost-Sensitive Boosting (ICML 1999)</strong></p> <ul dir="auto"> <li>Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip K. Chan</li> <li><a href="https://pdfs.semanticscholar.org/9ddf/bc2cc5c1b13b80a1a487b9caa57e80edd863.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting a Strong Learner: Evidence Against the Minimum Margin (ICML 1999)</strong></p> <ul dir="auto"> <li>Michael Bonnell Harries</li> <li><a href="https://dl.acm.org/citation.cfm?id=657480" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting Algorithms as Gradient Descent (NIPS 1999)</strong></p> <ul dir="auto"> <li>Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean</li> <li><a href="https://papers.nips.cc/paper/1766-boosting-algorithms-as-gradient-descent.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Boosting with Multi-Way Branching in Decision Trees (NIPS 1999)</strong></p> <ul dir="auto"> <li>Yishay Mansour, David A. McAllester</li> <li><a href="https://papers.nips.cc/paper/1659-boosting-with-multi-way-branching-in-decision-trees.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Potential Boosters (NIPS 1999)</strong></p> <ul dir="auto"> <li>Nigel Duffy, David P. Helmbold</li> <li><a href="https://pdfs.semanticscholar.org/4884/c765b6ceab7bdfb6703489810c8a386fd2a8.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">1998</h2><a id="user-content-1998" class="anchor" aria-label="Permalink: 1998" href="#1998"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>An Efficient Boosting Algorithm for Combining Preferences (ICML 1998)</strong></p> <ul dir="auto"> <li>Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer</li> <li><a href="http://jmlr.csail.mit.edu/papers/volume4/freund03a/freund03a.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Query Learning Strategies Using Boosting and Bagging (ICML 1998)</strong></p> <ul dir="auto"> <li>Naoki Abe, Hiroshi Mamitsuka</li> <li><a href="https://www.bic.kyoto-u.ac.jp/pathway/mami/pubs/Files/icml98.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Regularizing AdaBoost (NIPS 1998)</strong></p> <ul dir="auto"> <li>Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller</li> <li><a href="https://pdfs.semanticscholar.org/0afc/9de245547c675d40ad29240e2788c0416f91.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">1997</h2><a id="user-content-1997" class="anchor" aria-label="Permalink: 1997" href="#1997"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods (ICML 1997)</strong></p> <ul dir="auto"> <li>Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee</li> <li><a href="https://www.cc.gatech.edu/~isbell/tutorials/boostingmargins.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Using Output Codes to Boost Multiclass Learning Problems (ICML 1997)</strong></p> <ul dir="auto"> <li>Robert E. Schapire</li> <li><a href="http://rob.schapire.net/papers/Schapire97.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Improving Regressors Using Boosting Techniques (ICML 1997)</strong></p> <ul dir="auto"> <li>Harris Drucker</li> <li><a href="https://pdfs.semanticscholar.org/8d49/e2dedb817f2c3330e74b63c5fc86d2399ce3.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Pruning Adaptive Boosting (ICML 1997)</strong></p> <ul dir="auto"> <li>Dragos D. Margineantu, Thomas G. Dietterich</li> <li><a href="https://pdfs.semanticscholar.org/b25f/615fc139fbdeccc3bcf4462f908d7f8e37f9.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> <li> <p dir="auto"><strong>Training Methods for Adaptive Boosting of Neural Networks (NIPS 1997)</strong></p> <ul dir="auto"> <li>Holger Schwenk, Yoshua Bengio</li> <li><a href="https://papers.nips.cc/paper/1335-training-methods-for-adaptive-boosting-of-neural-networks.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">1996</h2><a id="user-content-1996" class="anchor" aria-label="Permalink: 1996" href="#1996"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><strong>Experiments with a New Boosting Algorithm (ICML 1996)</strong> <ul dir="auto"> <li>Yoav Freund, Robert E. Schapire</li> <li><a href="https://cseweb.ucsd.edu/~yfreund/papers/boostingexperiments.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">1995</h2><a id="user-content-1995" class="anchor" aria-label="Permalink: 1995" href="#1995"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><strong>Boosting Decision Trees (NIPS 1995)</strong> <ul dir="auto"> <li>Harris Drucker, Corinna Cortes</li> <li><a href="https://papers.nips.cc/paper/1059-boosting-decision-trees.pdf" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">1994</h2><a id="user-content-1994" class="anchor" aria-label="Permalink: 1994" href="#1994"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><strong>Boosting and Other Machine Learning Algorithms (ICML 1994)</strong> <ul dir="auto"> <li>Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik</li> <li><a href="https://www.sciencedirect.com/science/article/pii/B9781558603356500155" rel="nofollow">[Paper]</a></li> </ul> </li> </ul> <hr> <p dir="auto"><strong>License</strong></p> <ul dir="auto"> <li><a href="https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers/blob/master/LICENSE">CC0 Universal</a></li> </ul> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="W/4TBVwWONbfGKk/dzCfKdZMZhXDAogcQL0q1GrlCc/bmz37rnZYXiMYzWiq70Z0zQI9M9i2vCwEKTzsOhxqzA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> A curated list of gradient boosting research papers with implementations. </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/classifier" title="Topic: classifier" data-view-component="true" class="topic-tag topic-tag-link"> classifier </a> <a href="/topics/machine-learning" title="Topic: machine-learning" data-view-component="true" class="topic-tag topic-tag-link"> machine-learning </a> <a href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a href="/topics/random-forest" title="Topic: random-forest" data-view-component="true" class="topic-tag topic-tag-link"> random-forest </a> <a href="/topics/h2o" title="Topic: h2o" data-view-component="true" class="topic-tag topic-tag-link"> h2o </a> <a href="/topics/xgboost" title="Topic: xgboost" data-view-component="true" class="topic-tag topic-tag-link"> xgboost </a> <a href="/topics/lightgbm" title="Topic: lightgbm" data-view-component="true" class="topic-tag topic-tag-link"> lightgbm </a> <a href="/topics/gradient-boosting-machine" title="Topic: gradient-boosting-machine" data-view-component="true" class="topic-tag topic-tag-link"> gradient-boosting-machine </a> <a href="/topics/adaboost" title="Topic: adaboost" data-view-component="true" class="topic-tag topic-tag-link"> adaboost </a> <a href="/topics/decision-tree" title="Topic: decision-tree" data-view-component="true" class="topic-tag topic-tag-link"> decision-tree </a> <a href="/topics/gradient-boosting-classifier" title="Topic: gradient-boosting-classifier" data-view-component="true" class="topic-tag topic-tag-link"> gradient-boosting-classifier </a> <a href="/topics/classification-algorithm" title="Topic: classification-algorithm" data-view-component="true" class="topic-tag topic-tag-link"> classification-algorithm </a> <a href="/topics/gradient-boosting" title="Topic: gradient-boosting" data-view-component="true" class="topic-tag topic-tag-link"> gradient-boosting </a> <a href="/topics/boosting" title="Topic: boosting" data-view-component="true" class="topic-tag topic-tag-link"> boosting </a> <a href="/topics/classification-trees" title="Topic: classification-trees" data-view-component="true" class="topic-tag topic-tag-link"> classification-trees </a> <a href="/topics/xgboost-algorithm" title="Topic: xgboost-algorithm" data-view-component="true" class="topic-tag topic-tag-link"> xgboost-algorithm </a> <a href="/topics/catboost" title="Topic: catboost" data-view-component="true" class="topic-tag topic-tag-link"> catboost </a> <a href="/topics/gradient-boosted-trees" title="Topic: gradient-boosted-trees" data-view-component="true" class="topic-tag topic-tag-link"> gradient-boosted-trees </a> <a href="/topics/classification-tree" title="Topic: classification-tree" data-view-component="true" class="topic-tag topic-tag-link"> classification-tree </a> <a href="/topics/gradient-boosting-decision-trees" title="Topic: gradient-boosting-decision-trees" data-view-component="true" class="topic-tag topic-tag-link"> gradient-boosting-decision-trees </a> </div> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{"category":"Repository Overview","action":"click","label":"location:sidebar;file:readme"}" href="#readme-ov-file"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-book mr-2"> <path d="M0 1.75A.75.75 0 0 1 .75 1h4.253c1.227 0 2.317.59 3 1.501A3.743 3.743 0 0 1 11.006 1h4.245a.75.75 0 0 1 .75.75v10.5a.75.75 0 0 1-.75.75h-4.507a2.25 2.25 0 0 0-1.591.659l-.622.621a.75.75 0 0 1-1.06 0l-.622-.621A2.25 2.25 0 0 0 5.258 13H.75a.75.75 0 0 1-.75-.75Zm7.251 10.324.004-5.073-.002-2.253A2.25 2.25 0 0 0 5.003 2.5H1.5v9h3.757a3.75 3.75 0 0 1 1.994.574ZM8.755 4.75l-.004 7.322a3.752 3.752 0 0 1 1.992-.572H14.5v-9h-3.495a2.25 2.25 0 0 0-2.25 2.25Z"></path> </svg> Readme </a> </div> <h3 class="sr-only">License</h3> <div class="mt-2"> <a href="#CC0-1.0-1-ov-file" class="Link--muted" data-analytics-event="{"category":"Repository Overview","action":"click","label":"location:sidebar;file:license"}" > <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-law mr-2"> <path d="M8.75.75V2h.985c.304 0 .603.08.867.231l1.29.736c.038.022.08.033.124.033h2.234a.75.75 0 0 1 0 1.5h-.427l2.111 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.006.005-.01.01-.045.04c-.21.176-.441.327-.686.45C14.556 10.78 13.88 11 13 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L12.178 4.5h-.162c-.305 0-.604-.079-.868-.231l-1.29-.736a.245.245 0 0 0-.124-.033H8.75V13h2.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1 0-1.5h2.5V3.5h-.984a.245.245 0 0 0-.124.033l-1.289.737c-.265.15-.564.23-.869.23h-.162l2.112 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.016.015-.045.04c-.21.176-.441.327-.686.45C4.556 10.78 3.88 11 3 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L2.178 4.5H1.75a.75.75 0 0 1 0-1.5h2.234a.249.249 0 0 0 .125-.033l1.288-.737c.265-.15.564-.23.869-.23h.984V.75a.75.75 0 0 1 1.5 0Zm2.945 8.477c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L13 6.327Zm-10 0c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L3 6.327Z"></path> </svg> CC0-1.0 license </a> </div> <h3 class="sr-only">Code of conduct</h3> <div class="mt-2"> <a href="#coc-ov-file" class="Link--muted" data-analytics-event="{"category":"Repository Overview","action":"click","label":"location:sidebar;file:code of conduct"}" > <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-code-of-conduct mr-2"> <path d="M8.048 2.241c.964-.709 2.079-1.238 3.325-1.241a4.616 4.616 0 0 1 3.282 1.355c.41.408.757.86.996 1.428.238.568.348 1.206.347 1.968 0 2.193-1.505 4.254-3.081 5.862-1.496 1.526-3.213 2.796-4.249 3.563l-.22.163a.749.749 0 0 1-.895 0l-.221-.163c-1.036-.767-2.753-2.037-4.249-3.563C1.51 10.008.007 7.952.002 5.762a4.614 4.614 0 0 1 1.353-3.407C3.123.585 6.223.537 8.048 2.24Zm-1.153.983c-1.25-1.033-3.321-.967-4.48.191a3.115 3.115 0 0 0-.913 2.335c0 1.556 1.109 3.24 2.652 4.813C5.463 11.898 6.96 13.032 8 13.805c.353-.262.758-.565 1.191-.905l-1.326-1.223a.75.75 0 0 1 1.018-1.102l1.48 1.366c.328-.281.659-.577.984-.887L9.99 9.802a.75.75 0 1 1 1.019-1.103l1.384 1.28c.295-.329.566-.661.81-.995L12.92 8.7l-1.167-1.168c-.674-.671-1.78-.664-2.474.03-.268.269-.538.537-.802.797-.893.882-2.319.843-3.185-.032-.346-.35-.693-.697-1.043-1.047a.75.75 0 0 1-.04-1.016c.162-.191.336-.401.52-.623.62-.748 1.356-1.637 2.166-2.417Zm7.112 4.442c.313-.65.491-1.293.491-1.916v-.001c0-.614-.088-1.045-.23-1.385-.143-.339-.357-.633-.673-.949a3.111 3.111 0 0 0-2.218-.915c-1.092.003-2.165.627-3.226 1.602-.823.755-1.554 1.637-2.228 2.45l-.127.154.562.566a.755.755 0 0 0 1.066.02l.794-.79c1.258-1.258 3.312-1.31 4.594-.032.396.394.792.791 1.173 1.173Z"></path> </svg> Code of conduct </a> </div> <include-fragment src="/benedekrozemberczki/awesome-gradient-boosting-papers/hovercards/citation/sidebar_partial?tree_name=master"> </include-fragment> <div class="mt-2"> <a href="/benedekrozemberczki/awesome-gradient-boosting-papers/activity" data-view-component="true" class="Link Link--muted"><svg text="gray" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-pulse mr-2"> <path d="M6 2c.306 0 .582.187.696.471L10 10.731l1.304-3.26A.751.751 0 0 1 12 7h3.25a.75.75 0 0 1 0 1.5h-2.742l-1.812 4.528a.751.751 0 0 1-1.392 0L6 4.77 4.696 8.03A.75.75 0 0 1 4 8.5H.75a.75.75 0 0 1 0-1.5h2.742l1.812-4.529A.751.751 0 0 1 6 2Z"></path> </svg> <span class="color-fg-muted">Activity</span></a> </div> <h3 class="sr-only">Stars</h3> <div class="mt-2"> <a href="/benedekrozemberczki/awesome-gradient-boosting-papers/stargazers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>1k</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/benedekrozemberczki/awesome-gradient-boosting-papers/watchers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>49</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/benedekrozemberczki/awesome-gradient-boosting-papers/forks" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>158</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fbenedekrozemberczki%2Fawesome-gradient-boosting-papers&report=benedekrozemberczki+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/benedekrozemberczki/awesome-gradient-boosting-papers/releases" data-view-component="true" class="Link--primary no-underline Link">Releases <span title="3" data-view-component="true" class="Counter">3</span></a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/benedekrozemberczki/awesome-gradient-boosting-papers/releases/tag/v_0003"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">CIKM 2021</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2021-11-27T11:41:10Z" class="no-wrap">Nov 27, 2021</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/benedekrozemberczki/awesome-gradient-boosting-papers/releases" data-view-component="true" class="Link">+ 2 releases</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Sponsor this project</h2> <include-fragment src="/benedekrozemberczki/awesome-gradient-boosting-papers/sponsors_list?block_button=true&current_repository=awesome-gradient-boosting-papers" aria-busy="true" aria-label="Loading sponsorable links"> <div class="d-flex mb-3"> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> <div class="Skeleton Skeleton--text flex-1 flex-self-center f4"> </div> </div> <button type="button" disabled="disabled" data-view-component="true" class="btn btn-block"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-heart icon-sponsor mr-1 color-fg-sponsors"> <path d="m8 14.25.345.666a.75.75 0 0 1-.69 0l-.008-.004-.018-.01a7.152 7.152 0 0 1-.31-.17 22.055 22.055 0 0 1-3.434-2.414C2.045 10.731 0 8.35 0 5.5 0 2.836 2.086 1 4.25 1 5.797 1 7.153 1.802 8 3.02 8.847 1.802 10.203 1 11.75 1 13.914 1 16 2.836 16 5.5c0 2.85-2.045 5.231-3.885 6.818a22.066 22.066 0 0 1-3.744 2.584l-.018.01-.006.003h-.002ZM4.25 2.5c-1.336 0-2.75 1.164-2.75 3 0 2.15 1.58 4.144 3.365 5.682A20.58 20.58 0 0 0 8 13.393a20.58 20.58 0 0 0 3.135-2.211C12.92 9.644 14.5 7.65 14.5 5.5c0-1.836-1.414-3-2.75-3-1.373 0-2.609.986-3.029 2.456a.749.749 0 0 1-1.442 0C6.859 3.486 5.623 2.5 4.25 2.5Z"></path> </svg> Sponsor </button></include-fragment> <div class="text-small mt-3"> <a href="/sponsors">Learn more about GitHub Sponsors</a> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/users/benedekrozemberczki/packages?repo_name=awesome-gradient-boosting-papers" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Packages <span title="0" hidden="hidden" data-view-component="true" class="Counter ml-1">0</span></a></h2> <div class="text-small color-fg-muted" > No packages published <br> </div> </div> </div> <div class="BorderGrid-row" hidden> <div class="BorderGrid-cell"> <include-fragment src="/benedekrozemberczki/awesome-gradient-boosting-papers/used_by_list" accept="text/fragment+html"> </include-fragment> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Languages</h2> <div class="mb-2"> <span data-view-component="true" class="Progress"> <span style="background-color:#3572A5 !important;;width: 100.0%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> </span></div> <ul class="list-style-none"> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/benedekrozemberczki/awesome-gradient-boosting-papers/search?l=python" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3572A5;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Python</span> <span>100.0%</span> </a> </li> </ul> </div> </div> </div> </div> </div></div> </div> </div> </turbo-frame> </main> </div> </div> <footer class="footer pt-8 pb-6 f6 color-fg-muted p-responsive" role="contentinfo" > <h2 class='sr-only'>Footer</h2> <div class="d-flex flex-justify-center flex-items-center flex-column-reverse flex-lg-row flex-wrap flex-lg-nowrap"> <div class="d-flex flex-items-center flex-shrink-0 mx-2"> <a aria-label="Homepage" title="GitHub" class="footer-octicon mr-2" href="https://github.com"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-mark-github"> <path d="M12 1C5.9225 1 1 5.9225 1 12C1 16.8675 4.14875 20.9787 8.52125 22.4362C9.07125 22.5325 9.2775 22.2025 9.2775 21.9137C9.2775 21.6525 9.26375 20.7862 9.26375 19.865C6.5 20.3737 5.785 19.1912 5.565 18.5725C5.44125 18.2562 4.905 17.28 4.4375 17.0187C4.0525 16.8125 3.5025 16.3037 4.42375 16.29C5.29 16.2762 5.90875 17.0875 6.115 17.4175C7.105 19.0812 8.68625 18.6137 9.31875 18.325C9.415 17.61 9.70375 17.1287 10.02 16.8537C7.5725 16.5787 5.015 15.63 5.015 11.4225C5.015 10.2262 5.44125 9.23625 6.1425 8.46625C6.0325 8.19125 5.6475 7.06375 6.2525 5.55125C6.2525 5.55125 7.17375 5.2625 9.2775 6.67875C10.1575 6.43125 11.0925 6.3075 12.0275 6.3075C12.9625 6.3075 13.8975 6.43125 14.7775 6.67875C16.8813 5.24875 17.8025 5.55125 17.8025 5.55125C18.4075 7.06375 18.0225 8.19125 17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 15.5063 22.4362C19.8513 20.9787 23 16.8537 23 12C23 5.9225 18.0775 1 12 1Z"></path> </svg> </a> <span> © 2025 GitHub, Inc. </span> </div> <nav aria-label="Footer"> <h3 class="sr-only" id="sr-footer-heading">Footer navigation</h3> <ul class="list-style-none d-flex flex-justify-center flex-wrap mb-2 mb-lg-0" aria-labelledby="sr-footer-heading"> <li class="mx-2"> <a data-analytics-event="{"category":"Footer","action":"go to Terms","label":"text:terms"}" href="https://docs.github.com/site-policy/github-terms/github-terms-of-service" data-view-component="true" class="Link--secondary Link">Terms</a> </li> <li class="mx-2"> <a data-analytics-event="{"category":"Footer","action":"go to privacy","label":"text:privacy"}" href="https://docs.github.com/site-policy/privacy-policies/github-privacy-statement" data-view-component="true" class="Link--secondary Link">Privacy</a> </li> <li class="mx-2"> <a data-analytics-event="{"category":"Footer","action":"go to security","label":"text:security"}" href="https://github.com/security" data-view-component="true" class="Link--secondary Link">Security</a> </li> <li class="mx-2"> <a data-analytics-event="{"category":"Footer","action":"go to status","label":"text:status"}" href="https://www.githubstatus.com/" data-view-component="true" class="Link--secondary Link">Status</a> </li> <li class="mx-2"> <a data-analytics-event="{"category":"Footer","action":"go to docs","label":"text:docs"}" href="https://docs.github.com/" data-view-component="true" class="Link--secondary Link">Docs</a> </li> <li class="mx-2"> <a data-analytics-event="{"category":"Footer","action":"go to contact","label":"text:contact"}" href="https://support.github.com?tags=dotcom-footer" data-view-component="true" class="Link--secondary Link">Contact</a> </li> <li class="mx-2" > <cookie-consent-link> <button type="button" class="Link--secondary underline-on-hover border-0 p-0 color-bg-transparent" data-action="click:cookie-consent-link#showConsentManagement" data-analytics-event="{"location":"footer","action":"cookies","context":"subfooter","tag":"link","label":"cookies_link_subfooter_footer"}" > Manage cookies </button> </cookie-consent-link> </li> <li class="mx-2"> <cookie-consent-link> <button type="button" class="Link--secondary underline-on-hover border-0 p-0 color-bg-transparent" data-action="click:cookie-consent-link#showConsentManagement" data-analytics-event="{"location":"footer","action":"dont_share_info","context":"subfooter","tag":"link","label":"dont_share_info_link_subfooter_footer"}" > Do not share my personal information </button> </cookie-consent-link> </li> </ul> </nav> </div> </footer> <ghcc-consent id="ghcc" class="position-fixed bottom-0 left-0" style="z-index: 999999" data-initial-cookie-consent-allowed="" data-cookie-consent-required="false"></ghcc-consent> <div id="ajax-error-message" class="ajax-error-message flash flash-error" hidden> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-alert"> <path d="M6.457 1.047c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0 1 14.082 15H1.918a1.75 1.75 0 0 1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>