Stochastic Bandits
Stochastic Bandits is a sequential decision-making framework in which a learner repeatedly selects actions from a fixed set of alternatives and receives noisy reward feedback, with the defining characteristic that the rewards associated with each action are drawn independently from fixed but unknown probability distributions.
618
Publications
44.3K
Citations
940
Authors
373
Institutions
Publications per year
2017–2026
251
Variants
Stochastic Multi-armed Bandits
618
The Nonstochastic Multiarmed Bandit Problem
Peter Auer, Nicolò Cesa‐Bianchi, Yoav Freund et al. · SIAM Journal on Computing · 2002 · 2.3K citations
Zhe Cao, Tao Qin, Tie‐Yan Liu et al. · 2007 · 2K citations
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Massachusetts Institute of Technology
Cambridge, United States
20 h-index
34 publications
2K citations

Cornell University
Ithaca, United States
19 h-index
33 publications
2.9K citations

Centre National de la Recherche Scientifique
Paris, France
18 h-index
32 publications
1.9K citations
Princeton University
Princeton, United States
18 h-index
41 publications
4.5K citations

University of Alberta
Edmonton, Canada
17 h-index
38 publications
4.3K citations
University of Southern California
Los Angeles, United States
15 h-index
37 publications
3.1K citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
14 h-index
31 publications
3K citations
Stanford University
Stanford, United States
14 h-index
24 publications
2K citations
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Repository · 166 publications · 7K citations
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Conference · 14 publications · 419 citations
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13 publications · 1.3K citations
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AAP · 14 publications · 1.2K citations
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15 publications · 1.5K citations
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