We present a novel angle for multi-armed bandits (henceforth abbreviated MAB) which follows from the recent work on \\emphMAB mechanisms \\citepMechMAB-ec09,DevanurK08,Transform-ec10. The new problem is, essentially, about designing MAB algorithms under an additional constraint motivated by their application to MAB mechanisms.This note is self-contained, although some familiarity with MAB is assumed; we refer the reader to \\citeCesaBL-book for more background.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present a novel angle for multi-armed bandits (henceforth abbreviated MAB) which follows from the recent work on \\emphMAB mechanisms \\citepMechMAB-ec09,DevanurK08,Transform-ec10. The new problem is, essentially, about designing MAB algorithms under an additional constraint motivated by their application to MAB mechanisms.This note is self-contained, although some familiarity with MAB is assumed; we refer the reader […]<\/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-165787","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proceedings of the 24th Annual Conference on Learning Theory","msr_affiliation":"","msr_published_date":"2011-04-07","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":"http:\/\/proceedings.mlr.press\/v19\/slivkins11b.html","msr_doi":"","msr_publication_uploader":[{"type":"url","title":"http:\/\/proceedings.mlr.press\/v19\/slivkins11b.html","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/proceedings.mlr.press\/v19\/slivkins11b.html"}],"msr-author-ordering":[{"type":"text","value":"Aleksandrs Slivkins","user_id":0,"rest_url":false},{"type":"user_nicename","value":"slivkins","user_id":33685,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=slivkins"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144902],"msr_project":[171233],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":171233,"post_title":"Explore-Exploit Learning @MSR-NYC","post_name":"explore-exploit-learning","post_type":"msr-project","post_date":"2013-10-24 16:52:27","post_modified":"2017-08-10 13:39:37","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/explore-exploit-learning\/","post_excerpt":"This is an umbrella project for machine learning with explore-exploit tradeoff: the trade-off between acquiring and using information. This is a mature, yet very active, research area studied in Machine Learning, Theoretical Computer Science, Operations Research, and Economics. Much of our activity focuses on \"multi-armed bandits\" and \"contextual bandits\", relatively simple and yet very powerful models for explore-exploit tradeoff. We are located in (or heavily collaborating with)\u00a0Microsoft Research New York City. 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