Multi-armed Bandit Optimization
Multi-Armed Bandit Optimization is a sequential decision-making framework in personalization analytics that dynamically balances exploration and exploitation to maximize cumulative rewards by allocating traffic or resources across competing alternatives based on observed performance outcomes.
591
Publications
31.7K
Citations
1.3K
Authors
503
Institutions
Publications per year
2017–2026
303
Parents
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1–5 of 591
1.3K
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University of Southern California, United States
11 publications · 1K citations
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Tsinghua University
Beijing, China
18 h-index
50 publications
5K citations

Cornell University
Ithaca, United States
17 h-index
26 publications
2.8K citations
Microsoft (United States)
Redmond, United States
16 h-index
26 publications
3K citations

Centre National de la Recherche Scientifique
Paris, France
13 h-index
26 publications
901 citations
University of Michigan
Ann Arbor, United States
13 h-index
24 publications
1.1K citations
Carnegie Mellon University
Pittsburgh, United States
13 h-index
37 publications
1.6K citations
University of California, Berkeley
Berkeley, United States
12 h-index
28 publications
3K citations

Shanghai Jiao Tong University
Shanghai, China
12 h-index
34 publications
922 citations
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Repository · 72 publications · 2.1K citations
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18 publications · 817 citations
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Conference · 14 publications · 720 citations
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13 publications · 786 citations
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9 publications · 842 citations
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