{"id":580369,"date":"2007-12-03T09:00:38","date_gmt":"2007-12-03T17:00:38","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=580369"},"modified":"2019-04-19T10:55:46","modified_gmt":"2019-04-19T17:55:46","slug":"the-epoch-greedy-algorithm-for-contextual-multi-armed-bandits","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/the-epoch-greedy-algorithm-for-contextual-multi-armed-bandits\/","title":{"rendered":"The epoch-greedy algorithm for contextual multi-armed bandits"},"content":{"rendered":"

We present Epoch-Greedy, an algorithm for multi-armed bandits with observable side information. Epoch-Greedy has the following properties: No knowledge of a time horizon T<\/strong><\/em> is necessary. The regret incurred by Epoch-Greedy is controlled by a sample complexity bound for a hypothesis class. The regret scales as O<\/strong><\/em>(T<\/strong><\/em>2\/3 S<\/strong><\/em>1\/3) or better (sometimes, much better). Here S<\/strong><\/em> is the complexity term in a sample complexity bound for standard supervised learning.<\/p>\n","protected":false},"excerpt":{"rendered":"

We present Epoch-Greedy, an algorithm for multi-armed bandits with observable side information. Epoch-Greedy has the following properties: No knowledge of a time horizon T is necessary. The regret incurred by Epoch-Greedy is controlled by a sample complexity bound for a hypothesis class. The regret scales as O(T2\/3 S1\/3) or better (sometimes, much better). Here S […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-580369","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2007-12","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"http:\/\/papers.nips.cc\/paper\/3178-the-epoch-greedy-algorithm-for-multi-armed-bandits-with-side-information.pdf","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"John Langford","user_id":32204,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=John Langford"},{"type":"text","value":"Tong Zhang","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199571],"msr_event":[],"msr_group":[395930],"msr_project":[568491],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":568491,"post_title":"Real World Reinforcement Learning","post_name":"real-world-reinforcement-learning","post_type":"msr-project","post_date":"2019-05-03 10:02:09","post_modified":"2024-01-16 11:11:48","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/real-world-reinforcement-learning\/","post_excerpt":"The mission of Real World Reinforcement Learning (Real-World RL) team is to develop learning methods, from foundations to real world applications, to empower people and organizations to make better decisions. 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