Asymptotically efficient adaptive allocation rules
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UCB-based algorithms have been shown to be asymptotically efficient in reducing expected regret.
“algorithms based on this UCB approach have been shown to be asymptotically efficient in terms of reducing expected regret (Lai and Robbins, 1985; Li et al., 2017; Abbasi-Yadkori et al., 2019).”
From Hiring as Exploration (2020) · cites this paper as Lai and Robbins, 1985
Standard and contextual bandit UCB algorithms asymptotically minimize expected regret.
“Standard and contextual bandit UCB algorithms have been shown to be optimal in the sense that they asymptotically minimize expected regret (Lai and Robbins, 1985; Abbasi-Yadkori et al., 2019; Li et al., 2017)”
From Hiring as Exploration (2020) · cites this paper as Lai and Robbins, 1985
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- Authors
- Lai, T. L., & Robbins, H.
- Affiliations
- Columbia University (US)
- Venue
- Advances in Applied Mathematics
- Publisher
- Elsevier BV
- Published
- 1985-03
- Volume
- 6
- Issue
- 1
- Pages
- 4-22
- ISSN
- 0196-8858 (ISSN-L)
- OpenAlex
- W2009551863
- Cited by
- 2,406
- References
- 0
- Citations by year
- 2026: 60 · 2025: 108 · 2024: 118 · 2023: 122 · 2022: 155 · 2021: 252 · 2020: 322 · 2019: 182 · 2018: 156 · 2017: 101 · 2016: 98 · 2015: 108 · 2014: 112 · 2013: 92 · 2012: 90
- FWCI
- 3.87
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Advances in Applied Mathematics (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Keywords
- asymptotic efficiency, sequential decision making, optimal stopping
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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