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The Greedy algorithm is the simplest heuristic in sequential decision problem that carelessly takes the locally optimal choice at each round, disregarding any advantages of exploring and/or information gathering.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
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The continuum-armed bandit problem
Rajeev Agrawal · 1995
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Bandit problems with infinitely many arms
Donald A Berry, Robert W Chen, Alan Zame, David C Heath, and Larry A Shepp · 1997
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Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer · 2002
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Pac bounds for multi-armed bandit and markov decision processes
Eyal Even-Dar, Shie Mannor, and Yishay Mansour · 2002
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Nearly tight bounds for the continuum-armed bandit problem
Robert D Kleinberg · 2005
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Multi-armed bandit algorithms and empirical evaluation
Joannes Vermorel and Mehryar Mohri · 2005
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Improved rates for the stochastic continuum-armed bandit problem
Peter Auer, Ronald Ortner, and Csaba Szepesvári · 2007
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Anytime many-armed bandits
Olivier Teytaud, Sylvain Gelly, and Michele Sebag · 2007
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Multi-armed bandits in metric spaces
Robert Kleinberg, Aleksandrs Slivkins, and Eli Upfal · 2008
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Minimax policies for adversarial and stochastic bandits
Jean-Yves Audibert and Sébastien Bubeck · 2009
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Mortal multi-armed bandits
Deepayan Chakrabarti, Ravi Kumar, Filip Radlinski, and Eli Upfal · 2009
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A structured multiarmed bandit problem and the greedy policy
Adam J Mersereau, Paat Rusmevichientong, and John N Tsitsiklis · 2009
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Algorithms for infinitely many-armed bandits
Yizao Wang, Jean-Yves Audibert, and Rémi Munos · 2009
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Sébastien Bubeck, Rémi Munos, Gilles Stoltz, and Csaba Szepesvari · 2010
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An asymptotically optimal bandit algorithm for bounded support models
Junya Honda and Akimichi Takemura · 2010
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Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
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Lipschitz bandits without the lipschitz constant
Sébastien Bubeck, Gilles Stoltz, and Jia Yuan Yu · 2011
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Analysis of thompson sampling for the multi-armed bandit problem
Shipra Agrawal and Navin Goyal · 2012
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Regret analysis of stochastic and nonstochastic multi-armed bandit problems
Sébastien Bubeck and Nicolo Cesa-Bianchi · 2012
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Linear bandits in high dimension and recommendation systems
Yash Deshpande and Andrea Montanari · 2012
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Two-target algorithms for infinite-armed bandits with bernoulli rewards
Thomas Bonald and Alexandre Proutiere · 2013
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The multi-armed bandit problem with covariates
Vianney Perchet and Philippe Rigollet · 2013
Alberto Bietti, Alekh Agarwal, and John Langford · 2018
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Quantile-regret minimisation in infinitely many-armed bandits
Arghya Roy Chaudhuri and Shivaram Kalyanakrishnan · 2018
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A smoothed analysis of the greedy algorithm for the linear contextual bandit problem
Sampath Kannan, Jamie H Morgenstern, Aaron Roth, Bo Waggoner, and Zhiwei Steven Wu · 2018
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Adaptivity to smoothness in x-armed bandits
Andrea Locatelli and Alexandra Carpentier · 2018
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The externalities of exploration and how data diversity helps exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu · 2018
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Sub-sampling for multi-armed bandits
Akram Baransi, Odalric-Ambrym Maillard, and Shie Mannor · 2014
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Algorithms for multi-armed bandit problems
Volodymyr Kuleshov and Doina Precup · 2014
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Simple regret for infinitely many armed bandits
Alexandra Carpentier and Michal Valko · 2015
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Non-asymptotic analysis of a new bandit algorithm for semi-bounded rewards
Junya Honda and Akimichi Takemura · 2015
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Cascading bandits: Learning to rank in the cascade model
Branislav Kveton, Csaba Szepesvari, Zheng Wen, and Azin Ashkan · 2015
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Thompson sampling for budgeted multi-armed bandits
Yingce Xia, Haifang Li, Tao Qin, Nenghai Yu, and Tie-Yan Liu · 2015
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Daniel Russo and Benjamin Van Roy · 2018
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A thompson sampling algorithm for cascading bandits
Wang Chi Cheung, Vincent Tan, and Zixin Zhong · 2019
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Explore first, exploit next: The true shape of regret in bandit problems
Aurélien Garivier, Pierre Ménard, and Gilles Stoltz · 2019
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Polynomial cost of adaptation for x-armed bandits
Hédi Hadiji · 2019
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Semi-parametric sampling for stochastic bandits with many arms
Mingdong Ou, Nan Li, Cheng Yang, Shenghuo Zhu, and Rong Jin · 2019
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Exploiting structure of uncertainty for efficient matroid semi-bandits
Pierre Perrault, Vianney Perchet, and Michal Valko · 2019
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Introduction to multi-armed bandits
Aleksandrs Slivkins · 2019
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Unreasonable effectiveness of greedy algorithms in multi-armed bandit with many arms
Mohsen Bayati, Nima Hamidi, Ramesh Johari, and Khashayar Khosravi · 2020
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Lifelong learning in multi-armed bandits
Matthieu Jedor, Jonathan Louëdec, and Vianney Perchet · 2020
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Bandit algorithms
Tor Lattimore and Csaba Szepesvári · 2020
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Greedy algorithm almost dominates in smoothed contextual bandits
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu · 2020
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On regret with multiple best arms
Yinglun Zhu and Robert Nowak · 2020
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