Open problem: Adversarial multiarmed bandits with limited advice

, Crammer, Koby, & (2013) Open problem: Adversarial multiarmed bandits with limited advice. In Steinwart, I & Shalev-Shwartz, S (Eds.) JMLR: Workshop and Conference Proceedings, Volume 30: Conference on Learning Theory. Journal of Machine Learning Research - JMLR, United States of America, pp. 1067-1072.

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Description

Adversarial multiarmed bandits with expert advice is one of the fundamental problems in studying the exploration-exploitation trade-o. It is known that if we observe the advice of all experts on every round we can achieve O(√KTlnN) regret, where K is the number of arms, T is the number of game rounds, and N is the number of experts. It is also known that if we observe the advice of just one expert on every round, we can achieve regret of order O(√NT). Our open problem is what can be achieved by asking M experts on every round, where 1 < M < N.

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ID Code: 70844
Item Type: Chapter in Book, Report or Conference volume (Conference contribution)
Measurements or Duration: 6 pages
ISBN: 1938-7228
Pure ID: 32492318
Divisions: Past > QUT Faculties & Divisions > Science & Engineering Faculty
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Copyright Owner: CopyrightOwner{Copyright 2013 The Author(s)}CopyrightOwner
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Deposited On: 01 May 2014 01:34
Last Modified: 14 Mar 2024 00:45