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We consider nonstationary multi-armed bandit problems where the model parameters of the arms change over time.
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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Some aspects of the sequential design of experiments
Herbert Robbins · 1952
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Continuous inspection schemes
E. S. Page · 1954
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Inference about the change-point from cumulative sum tests
D. V. Hinkley · 1971
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Asymptotically efficient adaptive allocation rules
T.L. Lai and Herbert Robbins · 1985
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Gambling in a rigged casino: The adversarial multi-armed bandit problem
Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert E. Schapire · 1995
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Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer · 2002
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Learning with drift detection
João Gama, Pedro Medas, Gladys Castillo, and Pedro Pereira Rodrigues · 2004
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Early drift detection method
Manuel Baena-Garcıa, José del Campo-Ávila, Raúl Fidalgo, Albert Bifet, R Gavalda, and R Morales-Bueno · 2006
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Learning with local drift detection
João Gama and Gladys Castillo · 2006
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Discounted ucb
Levente Kocsis and Csaba Szepesvári · 2006
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Learning from time-changing data with adaptive windowing
Albert Bifet and Ricard Gavaldà · 2007
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Change Point Detection and Meta-Bandits for Online Learning in Dynamic Environments
Cédric Hartland, Nicolas Baskiotis, Sylvain Gelly, Michèle Sebag, and Olivier Teytaud · 2007
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Bandits Games and Clustering Foundations
Sébastien Bubeck · 2010
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A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E. Schapire · 2010
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An empirical evaluation of thompson sampling
Olivier Chapelle and Lihong Li · 2011
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The KL-UCB algorithm for bounded stochastic bandits and beyond
Aurélien Garivier and Olivier Cappé · 2011
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On upper-confidence bound policies for switching bandit problems
Aurélien Garivier and Eric Moulines · 2011
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Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms
Lihong Li, Wei Chu, John Langford, and Xuanhui Wang · 2011
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Thompson sampling: An asymptotically optimal finite-time analysis
Emilie Kaufmann, Nathaniel Korda, and Rémi Munos · 2012
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Exponentially weighted moving average charts for detecting concept drift
Gordon J. Ross, Niall M. Adams, Dimitris K. Tasoulis, and David J. Hand · 2012
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Further optimal regret bounds for thompson sampling
Shipra Agrawal and Navin Goyal · 2013
Ensemble learning for data stream analysis: A survey
Bartosz Krawczyk, Leandro L. Minku, João Gama, Jerzy Stefanowski, and Michal Wozniak · 2017
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Boundary crossing for general exponential families
Odalric-Ambrym Maillard · 2017
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A change-detection based framework for piecewise-stationary multi-armed bandit problem
Fang Liu, Joohyun Lee, and Ness B. Shroff · 2018
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On abruptly-changing and slowly-varying multiarmed bandit problems
Lai Wei and Vaibhav Srivastava · 2018
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Adaptively tracking the best bandit arm with an unknown number of distribution changes
Peter Auer, Pratik Gajane, and Ronald Ortner · 2019
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Thompson sampling in switching environments with bayesian online change detection
Joseph Charles Mellor and Jonathan Shapiro · 2013
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A survey on concept drift adaptation
João Gama, Indre Zliobaite, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia · 2014
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A comparative study on concept drift detectors
Paulo Mauricio Gonçalves, Silas Garrido Teixeira de Carvalho Santos, Roberto Souto Maior de Barros, and Davi Carnauba de Lima Vieira · 2014
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Stochastic multi-armed-bandit problem with non-stationary rewards
Yonatan Gur, Assaf J. Zeevi, and Omar Besbes · 2014
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Stream correlation monitoring for uncertainty-aware data processing systems
Aleka Seliniotaki, George Tzagkarakis, Vassilis Christophides, and Panagiotis Tsakalides · 2014
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Surveillance in an abruptly changing world via multiarmed bandits
Vaibhav Srivastava, Paul Reverdy, and Naomi Ehrich Leonard · 2014
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Omar Besbes, Yonatan Gur, and Assaf Zeevi · 2019
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Nearly optimal adaptive procedure with change detection for piecewise-stationary bandit
Yang Cao, Zheng Wen, Branislav Kveton, and Yao Xie · 2019
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A new algorithm for non-stationary contextual bandits: Efficient, optimal and parameter-free
Yifang Chen, Chung-Wei Lee, Haipeng Luo, and Chen-Yu Wei · 2019
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Scaling multi-armed bandit algorithms
Edouard Fouché, Junpei Komiyama, and Klemens Böhm · 2019
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Learning under concept drift: A review
Jie Lu, Anjin Liu, Fan Dong, Feng Gu, João Gama, and Guangquan Zhang · 2019
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Distribution-dependent and time-uniform bounds for piecewise i.i.d bandits
Subhojyoti Mukherjee and Odalric-Ambrym Maillard · 2019
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Efficient change-point detection for tackling piecewise-stationary bandits
Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard, and Julien Seznec · 2020
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A single algorithm for both restless and rested rotting bandits
Julien Seznec, Pierre Ménard, Alessandro Lazaric, and Michal Valko · 2020
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Sliding-Window Thompson Sampling for Non-Stationary Settings
Francesco Trovò, Marcello Restelli, and Nicola Gatti · 2020
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On slowly-varying non-stationary bandits
Ramakrishnan Krishnamurthy and Aditya Gopalan · 2021
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Open bandit dataset and pipeline: Towards realistic and reproducible off-policy evaluation
Yuta Saito, Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita · 2021
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