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ASPEST: Bridging the Gap Between Active Learning and Selective Prediction

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class="glue-grid"> <div class="glue-grid__col glue-grid__col--span-12 glue-grid__col--span-10-lg"> <h1 class="headline-2">ASPEST: Bridging the Gap Between Active Learning and Selective Prediction</h1> <div class="basic-hero--publication__authors"> <div class="basic-hero--publication__authors__item"> <a href="/people/jiefengchen/">Jiefeng Chen</a> </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> <a href="/people/106672/">Jinsung Yoon</a> </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> <a href="/people/saynaebrahimi/">Sayna Ebrahimi</a> </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> <a href="/people/106303/">Sercan Arik</a> </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> Somesh Jha </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> <a href="/people/105803/">Tomas Pfister</a> </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> Transactions on Machine Learning Research (TMLR) (2024) </div> </div> <div class="basic-hero--publication__buttons"> <a class="glue-button glue-button--high-emphasis glue-button--reversed glue-button--icon" href="https://openreview.net/pdf?id=3nprbNR3HB" target="_blank" rel="noreferrer noopener"> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#file-download"></use> </svg> <span class="js-gt-item-id">Download</span> </a> <a class="glue-button glue-button--medium-emphasis glue-button--reversed" href="https://scholar.google.com/scholar?lr&amp;ie=UTF-8&amp;oe=UTF-8&amp;q=ASPEST: Bridging the Gap Between Active Learning and Selective Prediction Jiefeng Chen Jinsung Yoon Sayna Ebrahimi Sercan Arik Somesh Jha Tomas Pfister" target="_blank" rel="noreferrer noopener"> <span class="js-gt-item-id">Google Scholar</span> </a> <div class="glue-copy" glue-copy-success="copied!" glue-copy-fail="Press Ctrl+C or ⌘+C to copy"> <input class="glue-copy-value" type="text" value="@article{53278, title = {ASPEST: Bridging the Gap Between Active Learning and Selective Prediction}, author = {Jiefeng Chen and Jinsung Yoon and Sayna Ebrahimi and Sercan Arik and Somesh Jha and Tomas Pfister}, year = {2024}, URL = {https://openreview.net/pdf?id=3nprbNR3HB}, journal = {Transactions on Machine Learning Research (TMLR)} } " aria-label="URL" readonly> <button class="glue-button glue-button--medium-emphasis glue-button--reversed glue-copy-button" aria-live='polite'> <span class="js-gt-item-id">Copy Bibtex</span> </button> </div> </div> </div> </div> </div> </section> <section class="offset-two-up --theme-light"> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h3 class="offset-two-up__headline headline-3">Abstract</h3> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. These predictions can then be deferred to humans for further evaluation. As an everlasting challenge for machine learning, in many real-world scenarios, the distribution of test data is different from the training data. This results in more inaccurate predictions, and often increased dependence on humans, which can be difficult and expensive. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. Selective prediction and active learning have been approached from different angles, with the connection between them missing. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain while increasing accuracy and coverage. For this new paradigm, we propose a simple yet effective approach, ASPEST, that utilizes ensembles of model snapshots with self-training with their aggregated outputs as pseudo labels. Extensive experiments on numerous image, text and structured datasets, which suffer from domain shifts, demonstrate that ASPEST can significantly outperform prior work on selective prediction and active learning (e.g. on the MNIST→SVHN benchmark with the labeling budget of 100, ASPEST improves the AUACC metric from 79.36% to 88.84%) and achieves more optimal utilization of humans in the loop. </div> </div> </section> <section class="offset-two-up --theme-light"> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h3 class="offset-two-up__headline headline-3">Research Areas</h3> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <ul class="card-stack--small nested-glue-grid-override" data-gt-id="research_areas_small_card_stack" data-gt-component-name="Resarch Areas - Small Card Stack"> <li class="glue-grid__col 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