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In bandit multiple hypothesis testing, each arm corresponds to a different null hypothesis that we wish to test, and the goal is to design adaptive algorithms that correctly identify large set of interesting arms (true discoveries), while only mistakenly identifying a few uninteresting ones (false discoveries).
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Online learning for changing environments using coin betting
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A Rejection Principle for Sequential Tests of Multiple Hypotheses Controlling Familywise Error Rates
J. Bartroff and J. Song · 2016
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W. Chen, Y. Wang, Y. Yuan, and Q. Wang · 2016
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E. Kaufmann, O. Cappé, and A. Garivier · 2016
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Coin betting and parameter-free online learning
F. Orabona and D. Pál · 2016
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Bandits on Graphs and Structures
M. Valko · 2016
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Multiple Hypothesis Tests Controlling Generalized Error Rates for Sequential Data
J. Bartroff · 2017
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Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration
L. Chen, A. Gupta, J. Li, M. Qiao, and R. Wang · 2017
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Probability: Theory and Examples
R. Durrett · 2017
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Training Deep Networks without Learning Rates Through Coin Betting
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P. Grünwald, R. de Heide, and W. Koolen · 2020
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Time-uniform chernoff bounds via nonnegative supermartingales
S. R. Howard, A. Ramdas, J. McAuliffe, and J. Sekhon · 2020
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A general interactive framework for false discovery rate control under structural constraints
L. Lei, A. Ramdas, and W. Fithian · 2020
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Admissible anytime-valid sequential inference must rely on nonnegative martingales
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