{"id":1692,"date":"2025-02-06T07:02:29","date_gmt":"2025-02-06T07:02:29","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/02\/06\/2502-02861\/"},"modified":"2025-02-06T07:02:29","modified_gmt":"2025-02-06T07:02:29","slug":"2502-02861","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/02\/06\/2502-02861\/","title":{"rendered":"Algorithms with Calibrated Machine Learning Predictions"},"content":{"rendered":"<p>    Algorithms with Calibrated Machine Learning Predictions<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2502.02861v1 Announce Type: new<br \/>\nAbstract: The field of algorithms with predictions incorporates machine learning advice in the design of online algorithms to improve real-world performance. While this theoretical framework often assumes uniform reliability across all predictions, modern machine learning models can now provide instance-level uncertainty estimates. In this paper, we propose calibration as a principled and practical tool to bridge this gap, demonstrating the benefits of calibrated advice through two case studies: the ski rental and online job scheduling problems. For ski rental, we design an algorithm that achieves optimal prediction-dependent performance and prove that, in high-variance settings, calibrated advice offers more effective guidance than alternative methods for uncertainty quantification. For job scheduling, we demonstrate that using a calibrated predictor leads to significant performance improvements over existing methods. Evaluations on real-world data validate our theoretical findings, highlighting the practical impact of calibration for algorithms with predictions.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Judy Shen, Ellen Vitercik, Anders Wikum<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2502.02861\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Algorithms with Calibrated Machine Learning Predictions arXiv:2502.02861v1 Announce Type: new Abstract: The field of algorithms with predictions incorporates machine learning advice in the design of online algorithms to improve real-world performance. While this theoretical framework often assumes uniform reliability across all predictions, modern machine learning models can now provide instance-level uncertainty estimates. In this paper, [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,413,113,112],"tags":[821,1656,1349],"class_list":["post-1692","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-ds","category-cs-lg","category-stat-ml","tag-algorithms","tag-calibrated","tag-predictions"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1692"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=1692"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1692\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=1692"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=1692"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=1692"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}