Bandit Algorithms
Bandit Algorithms is a class of sequential decision-making algorithms that address the exploration-exploitation tradeoff in stochastic environments by iteratively selecting actions to maximize cumulative rewards based on partial feedback.
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2017–2026
942
Variants
Mab AlgorithmsMulti-armed Bandit Algorithms
Parents
1.9K
A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford et al. · 2010 · 2.5K citations · Full text
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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Microsoft (United States)
Redmond, United States
40 h-index
144 publications
12.7K citations

Cornell University
Ithaca, United States
38 h-index
117 publications
8.7K citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
31 h-index
79 publications
6.1K citations
Princeton University
Princeton, United States
30 h-index
81 publications
7.9K citations

University of Alberta
Edmonton, Canada
30 h-index
72 publications
6.7K citations
Massachusetts Institute of Technology
Cambridge, United States
30 h-index
101 publications
6.4K citations
University of California, Berkeley
Berkeley, United States
29 h-index
89 publications
8.3K citations
Carnegie Mellon University
Pittsburgh, United States
27 h-index
119 publications
9.4K citations
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Repository · 435 publications · 19.2K citations
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Conference · 58 publications · 2.2K citations
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Conference · 40 publications · 1.4K citations
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22 publications · 1.9K citations
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Repository · 21 publications · 1.8K citations
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