Mistake bounds on the noise-free multi-armed bandit game


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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/83379

Title:	Mistake bounds on the noise-free multi-armed bandit game
Authors:	Nakamura, Atsuyoshi Browse this author →KAKEN DB
	Helmbold, David P. Browse this author
	Warmuth, Manfred K. Browse this author
Keywords:	Computational learning theory
	Online learning
	Bandit problem
	Mistake bound
Issue Date:	Dec-2019
Publisher:	Elsevier
Journal Title:	Information and computation
Volume:	269
Start Page:	104453
Publisher DOI:	10.1016/j.ic.2019.104453
Abstract:	We study the {0, 1}-loss version of adaptive adversarial multi-armed bandit problems with alpha(>= 1) lossless arms. For the problem, we show a tight bound K - alpha - Theta(1/T) on the minimax expected number of mistakes (1-losses), where K is the number of arms and T is the number of rounds. (C) 2019 Elsevier Inc. All rights reserved.
Rights:	© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Rights:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Type:	article (author version)
URI:	http://hdl.handle.net/2115/83379
Appears in Collections:	情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 中村篤祥

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