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Individual decision-makers consume information revealed by the previous decision makers, and produce information that may help in future decisions.
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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Application of the theory of martingales
Joseph L. Doob · 1949
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Probability inequalities for sums of bounded random variables
Wassily Hoeffding · 1963
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Bandit processes and dynamic allocation indices (with discussion)
J. C. Gittins · 1979
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A one-armed bandit problem with a concomitant variable
Michael Woodroofe · 1979
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Asymptotically efficient Adaptive Allocation Rules
Tze Leung Lai and Herbert Robbins · 1985
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Hoeffding races: Accelerating model selection search for classification and function approximation
Oded Maron and Andrew W. Moore · 1993
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The nonstochastic multiarmed bandit problem
Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert E. Schapire · 1995
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A review of consistency and convergence rates of posterior distribution, 1996
Subhashis Ghosal · 1996
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The racing algorithm: Model selection for lazy learners
Oded Maron and Andrew W. Moore · 1997
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Strategic Experimentation
Patrick Bolton and Christopher Harris · 1999
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Monitoring clinical trials with multiple arms
Martin Hellmich · 2001
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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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The value of knowing a demand curve: Bounds on regret for online posted-price auctions
Robert D. Kleinberg and Frank T. Leighton · 2003
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The sample complexity of exploration in the multi-armed bandit problem
Shie Mannor and John N. Tsitsiklis · 2004
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Adaptive signature design: An adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients
Boris Freidlin and Richard Simon · 2005
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Strategic Experimentation with Exponential Bandits
Godfrey Keller, Sven Rady, and Martin Cripps · 2005
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Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Michael Mitzenmacher and Eli Upfal · 2005
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Bandit problems with side observations
Chih-Chun Wang, Sanjeev R. Kulkarni, and H. Vincent Poor · 2005
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The dynamic pivot mechanism
Dirk Bergemann and Juuso Välimäki · 2006
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Prediction, learning, and games
Nicolò Cesa-Bianchi and Gábor Lugosi · 2006
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Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems
Eyal Even-Dar, Shie Mannor, and Yishay Mansour · 2006
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An efficient dynamic mechanism
Susan Athey and Ilya Segal · 2007
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Biomarker adaptive threshold design: A procedure for evaluating treatment with possible biomarker-defined subset effect
Boris Freidlin, Wenyu Jiang, and Richard Simon · 2007
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Approximation algorithms for budgeted learning problems
Sudipta Guha and Kamesh Munagala · 2007
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The on-line shortest path problem under partial monitoring
András György, Tamás Linder, Gábor Lugosi, and György Ottucsák · 2007
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The Epoch-Greedy Algorithm for Contextual Multi-armed Bandits
John Langford and Tong Zhang · 2007
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Adaptive design methods in clinical trials – a review
Shein-Chung Chow and Mark Chang · 2008
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Multi-arm clinical trials of new agents: Some design considerations
Boris Freidlin, Edward L. Korn, Robert Gray, and Alison Martin · 2008
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Regret Bounds and Minimax Policies under Partial Monitoring
J.Y. Audibert and S. Bubeck · 2009
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Characterizing truthful multi-armed bandit mechanisms
Moshe Babaioff, Yogeshwer Sharma, and Aleksandrs Slivkins · 2009
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Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms
Omar Besbes and Assaf Zeevi · 2009
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The price of truthfulness for pay-per-click auctions
Nikhil Devanur and Sham M. Kakade · 2009
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The Ratio Index for Budgeted Learning, with Applications
Ashish Goel, Sanjeev Khanna, and Brad Null · 2009
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Cross-validated adaptive signature design
Boris Freidlin, Wenyu Jiang, and Richard Simon · 2010
Cited alongside, same era.
Non-stochastic bandit slate problems
Satyen Kale, Lev Reyzin, and Robert E. Schapire · 2010
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Adaptive contract design for crowdsourcing markets: Bandit algorithms for repeated principal-agent problems
Chien-Ju Ho, Aleksandrs Slivkins, and Jennifer Wortman Vaughan · 2014
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Implementing the “wisdom of the crowd”
Ilan Kremer, Yishay Mansour, and Motty Perry · 2014
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More multiarm randomised trials of superiority are needed
Mahesh K.B. Parmar, James Carpenter, and Matthew R. Sydes · 2014
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Online learning with feedback graphs: Beyond bandits
Noga Alon, Nicolò Cesa-Bianchi, Ofer Dekel, and Tomer Koren · 2015
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Suspense and Surprise
Jeff Ely, Alex Frankel, and Emir Kamenica · 2015
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New oncology clinical trial designs: What works and what doesn’t?
Surabhidanhi Garimella · 2015
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Optimal Information Disclosure
Luis Rayo and Ilya Segal · 2010
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Minimax policies for combinatorial prediction games
Jean-Yves Audibert, Sébastien Bubeck, and Gábor Lugosi · 2011
Cited alongside, same era.
Pure Exploration in Multi-Armed Bandit Problems
Sébastien Bubeck, Rémi Munos, and Gilles Stoltz · 2011
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Efficient optimal leanring for contextual bandits
Miroslav Dudík, Daniel Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, and Tong Zhang · 2011
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Multi-Armed Bandit Allocation Indices
John Gittins, Kevin Glazebrook, and Richard Weber · 2011
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Bayesian Persuasion
Emir Kamenica and Matthew Gentzkow · 2011
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Selling Information
Johannes Hörner and Andrzej Skrzypacz · 2015
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Basket trials and the evolution of clinical trial design in an era of genomic medicine
Amanda Redig and Pasi Jänne · 2015
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Efficient learning in large-scale combinatorial semi-bandits
Zheng Wen, Branislav Kveton, and Azin Ashkan · 2015
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Economic recommendation systems
Gal Bahar, Rann Smorodinsky, and Moshe Tennenholtz · 2016
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Information design, bayesian persuasion and bayes correlated equilibrium, 2016
Dirk Bergemann and Stephen Morris · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth · 2016
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Descending price optimally coordinates search
Robert D. Kleinberg, Bo Waggoner, and E. Glen Weyl · 2016
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Bayesian exploration: Incentivizing exploration in Bayesian games
Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, and Steven Wu · 2016
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Information design, 2016
Ina Taneva · 2016
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L. Elisa Celis and Nisheeth K Vishnoi · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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Fairness incentives for myopic agents
Sampath Kannan, Michael Kearns, Jamie Morgenstern, Mallesh Pai, Aaron Roth, Rakesh Vohra, and Z Steven Wu · 2017
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Meritocratic fairness for cross-population selection
Michael Kearns, Aaron Roth, and Zhiwei Steven Wu · 2017
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Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
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Calibrated fairness in bandits
Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal, and David C Parkes · 2017
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Mostly exploration-free algorithms for contextual bandits
Hamsa Bastani, Mohsen Bayati, and Khashayar Khosravi · 2018
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Crowdsourcing exploration
Kostas Bimpikis, Yiangos Papanastasiou, and Nicos Savva · 2018
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Incentivizing exploration by heterogeneous users
Bangrui Chen, Peter I. Frazier, and David Kempe · 2018
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A smoothed analysis of the greedy algorithm for the linear contextual bandit problem
Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, and Zhiwei Steven Wu · 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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Human interaction with recommendation systems
Sven Schmit and Carlos Riquelme · 2018
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Bayesian exploration with heterogenous agents
Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, and Steven Wu · 2019
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