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= true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":108143845,"created_at":"2023-10-14T17:09:17.910-07:00","from_world_paper_id":241968897,"updated_at":"2024-11-23T13:45:45.279-08:00","_data":{"publisher":"Springer Science and Business Media LLC","grobid_abstract":"Bayesian optimization (BO) has recently emerged as a powerful and flexible tool for hyperparameter tuning and more generally for the efficient global optimization of expensive black-box functions. Systems implementing BO have successfully solved difficult problems in automatic design choices and machine learning hyper-parameters tunings. Many recent advances in the methodologies and theories underlying Bayesian optimization have extended the framework to new applications and provided greater insights into the behavior of these algorithms. Still, these established techniques always require a user-defined space to perform optimization. This pre-defined space specifies the ranges of hyper-parameter values. In many situations, however, it can be difficult to prescribe such spaces, as a prior knowledge is often unavailable. Setting these regions arbitrarily can lead to inefficient optimization-if a space is too large, we can miss the optimum with a limited budget, and on the other hand, if a space is too small, it may not contain the optimum point that we want to get. The unknown search space problem is intractable to solve in practice. Therefore, in this paper, we narrow down to consider specifically the setting of \"weakly specified\" search space for Bayesian optimization. By weakly specified space, we mean that the pre-defined space is placed at a sufficiently good region so that the optimization can expand and reach to the optimum. However, this pre-defined space need not include the global optimum. We tackle this problem by proposing the filtering expansion strategy for Bayesian optimization. Our approach starts from the initial region and gradually expands the search space. We develop an efficient algorithm for this strategy and derive its regret bound. These theoretical results are complemented by an extensive set of experiments on benchmark functions and two real-world applications which demonstrate the benefits of our proposed approach. Keywords Bayesian optimization • Unknown search space • Hyper-parameter tuning • Experimental design 1 Introduction Global optimization is fundamental to diverse real-world problems where parameter settings and design choices are pivotal-as an example, in algorithm hyper-parameter tuning [39,42]","publication_date":"2018,,","publication_name":"Knowledge and Information Systems","grobid_abstract_attachment_id":"106605930"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Filtering Bayesian optimization approach in weakly specified search space","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [214605324]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; 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We develop an algorithm for low budget optimization. We further optimize the hyperparameters of our algorithm using Bayesian optimization. Our approach has been ranked 3rd in the competition.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization","attachmentId":85894678,"attachmentType":"pdf","work_url":"https://www.academia.edu/79040450/Solving_Black_Box_Optimization_Challenge_via_Learning_Search_Space_Partition_for_Local_Bayesian_Optimization","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/79040450/Solving_Black_Box_Optimization_Challenge_via_Learning_Search_Space_Partition_for_Local_Bayesian_Optimization"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="79379868" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/79379868/Bayesian_Search_for_Robust_Optima">Bayesian Search for Robust Optima</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="31636764" href="https://independent.academia.edu/EversonR">Richard Everson</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Many expensive black-box optimisation problems are sensitive to their inputs. In these problems it makes more sense to locate a region of good designs, than a single—possibly fragile—optimal design. Expensive black-box functions can be optimised effectively with Bayesian optimisation, where a Gaussian process is a popular choice as a prior over the expensive function. We propose a method for robust optimisation using Bayesian optimisation to find a region of design space in which the expensive function’s performance is relatively insensitive to the inputs whilst retaining a good quality. This is achieved by sampling realisations from a Gaussian process that is modelling the expensive function, and evaluating the improvement for each realisation. The expectation of these improvements can be optimised cheaply with an evolutionary algorithm to determine the next location at which to evaluate the expensive function. We describe an efficient process to locate the optimum expected improve...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Bayesian Search for Robust Optima","attachmentId":86115022,"attachmentType":"pdf","work_url":"https://www.academia.edu/79379868/Bayesian_Search_for_Robust_Optima","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/79379868/Bayesian_Search_for_Robust_Optima"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="89020172" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/89020172/Hybrid_Batch_Bayesian_Optimization">Hybrid Batch Bayesian Optimization</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="225755520" href="https://independent.academia.edu/javadazimi6">javad azimi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2012</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Hybrid Batch Bayesian Optimization","attachmentId":92894781,"attachmentType":"pdf","work_url":"https://www.academia.edu/89020172/Hybrid_Batch_Bayesian_Optimization","alternativeTracking":true}"><span class="material-symbols-outlined" 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href="https://independent.academia.edu/JoseDorronsoro">Jose Dorronsoro</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Modified Grid Searches for Hyper-Parameter Optimization","attachmentId":110402413,"attachmentType":"pdf","work_url":"https://www.academia.edu/113445905/Modified_Grid_Searches_for_Hyper_Parameter_Optimization","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/113445905/Modified_Grid_Searches_for_Hyper_Parameter_Optimization"><span class="ds2-5-text-link__content">View PDF</span><span 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