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Permanent link (DOI): https://doi.org/10.7939/R3QQ78

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The Role of Information in Online Learning Open Access

Descriptions

Other title
Subject/Keyword
regret analysis
online learning
machine learning
partial monitoring
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Bartók, Gábor
Supervisor and department
Szepesvári, Csaba (Computing Science)
Examining committee member and department
Schuurmans, Dale (Computing Science)
Greiner, Russel (Computing Science)
Bowling, Michael (Computing Science)
Gombay, Edit (Mathematical and Statistical Sciences)
Ben-David, Shai (Computer Science, University of Waterloo)
Department
Department of Computing Science
Specialization

Date accepted
2012-07-06T15:00:01Z
Graduation date
2012-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
In a partial-monitoring game a player has to make decisions in a sequential manner. In each round, the player suffers some loss that depends on his decision and an outcome chosen by an opponent, after which he receives "some" information about the outcome. The goal of the player is to keep the sum of his losses as low as possible. This problem is an instance of online learning: By choosing his actions wisely the player can figure out important bits about the opponent's strategy that, in turn, can be used to select actions that will have small losses. Surprisingly, up to now, very little is known about this fundamental online learning problem. In this thesis, we investigate this problem. In particular, we investigate to what extent the information received influences the best achievable cumulative loss suffered by an optimal player. We present algorithms that have theoretical guarantees for achieving low cumulative loss, and prove their optimality by providing matching, algorithm independent lower bounds. Our new algorithms represent new ways of handling the exploration-exploitation trade-off, while some of the lower bound proofs introduce novel proof techniques.
Language
English
DOI
doi:10.7939/R3QQ78
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Gábor Bartók, Dávid Pál, and Csaba Szepesvári. Toward a classification of finite partial-monitoring games. In ALT'10, pages 224–238, 2010.Gábor Bartók, Dávid Pál, and Csaba Szepesvári. Minimax regret of finite partial-monitoring games in stochastic environments. In COLT 2011, Proceedings of the 24th Annual Conference on Learning Theory, Budapest, Hungary, July 9–11, 2011, 2011.Gábor Bartók, Navid Zolghadr, and Csaba Szepesvári. An adaptive algorithm for finite stochastic partial monitoring. In ICML, 2012.

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