The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Regret bounds for sleeping experts and bandits
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Citations
Cites
Finite-time Analysis of the Multiarmed Bandit Problem
Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002) · Machine Learning · doi:10.1023/a:1013689704352 · Cited by 6,033
Some aspects of the sequential design of experiments
Robbins, H. (1952) · Bulletin of the American Mathematical Society · doi:10.1090/s0002-9904-1952-09620-8 · Cited by 2,184
Cited by
Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce
Paraschakis, D. (2018) · doi:10.24834/2043/24268 · Cited by 35
A Sleeping, Recovering Bandit Algorithm for Optimizing Recurring Notifications
Yancey, K. P., & Settles, B. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403351 · Cited by 5
Algorithmic monoculture and social welfare
(2023) · The Societal Impacts of Algorithmic Decision-Making · doi:10.1145/3603195.3603202
Metadata
- Authors
- Kleinberg, R., Niculescu-Mizil, A., & Sharma, Y.
- Venue
- Machine Learning
- Published
- 2010-04-29
- Volume
- 80
- Issue
- 2-3
- Pages
- 245-272
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2008098735
- Cited by
- 198
- References
- 2
- FWCI
- 14.39
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Regret bounds for sleeping experts and bandits. https://app.cochat.ai/papers/10.1007/s10994-010-5178-7
- The paper
- Kleinberg, R., Niculescu-Mizil, A., & Sharma, Y. (2010). Regret bounds for sleeping experts and bandits. Machine Learning. https://doi.org/10.1007/s10994-010-5178-7
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
