Bayesian Bandits is a sequential decision-making framework that integrates Bayesian inference with multi-armed bandit problems to dynamically update beliefs about reward distributions and balance exploration and exploitation over time.
82
Publications
8.2K
Citations
184
Authors
88
Institutions
Publications per year
2017–2026
32
Publications
82
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo et al. · arXiv (Cornell University) · 2016 · 1.1K citations · Full text
1–5 of 82
Authors
184
- BP
3
h-index
- JS
3
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- KK
3
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- PI
3
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- DADonald A. Berry
The University of Texas MD Anderson Cancer Center, United States
3 publications · 88 citations
3
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1–5 of 184
Institutions
88
Carnegie Mellon University
Pittsburgh, United States
7 h-index
15 publications
5K citations
Microsoft (United States)
Redmond, United States
5 h-index
9 publications
362 citations
Google (United States)
Mountain View, United States
5 h-index
8 publications
4.6K citations
University of California, Berkeley
Berkeley, United States
4 h-index
6 publications
383 citations
Cornell University
Ithaca, United States
3 h-index
7 publications
707 citations
University of Oxford
Oxford, United Kingdom
3 h-index
4 publications
130 citations
University of Minnesota
Minneapolis, United States
3 h-index
4 publications
204 citations
Massachusetts Institute of Technology
Cambridge, United States
3 h-index
3 publications
186 citations
1–8 of 88
Venues
- arXiv (Cornell University)
Repository · 15 publications · 1.7K citations
11
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- Journal of the American Statistical Association
4 publications · 199 citations
4
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- Operations Research
3 publications · 203 citations
3
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- International Conference on Artificial Intelligence and Statistics
Conference · 2 publications · 100 citations
2
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- The Annals of Statistics
2 publications · 73 citations
2
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