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Thresholding Bandits with Augmented UCB

https://doi.org/10.24963/ijcai.2017/350
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s10998-010-3055-6openalex-snapshot
UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem
inferredcites →https://doi.org/10.1016/0196-8858(85)90002-8openalex-snapshot
Asymptotically efficient adaptive allocation rules
inferredcites →https://doi.org/10.1016/j.tcs.2009.01.016openalex-snapshot
Exploration–exploitation tradeoff using variance estimates in multi-armed bandits
inferredcites →https://doi.org/10.1016/j.tcs.2010.12.059openalex-snapshot
Pure exploration in finitely-armed and continuous-armed bandits
inferredcites →https://doi.org/10.1016/j.tcs.2016.06.034openalex-snapshot
Modification of improved upper confidence bounds for regulating exploration in Monte-Carlo tree search
inferredcites →https://doi.org/10.1090/s0002-9904-1952-09620-8openalex-snapshot
Some aspects of the sequential design of experiments
inferredcites →https://doi.org/10.1093/biomet/25.3-4.285openalex-snapshot
ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES
inferredcites →https://doi.org/10.1109/ciss.2014.6814096openalex-snapshot
Best-arm identification algorithms for multi-armed bandits in the fixed confidence setting
inferredcites →https://doi.org/10.1561/9781601986276openalex-snapshot
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
inferredcites →https://doi.org/10.48550/arxiv.1205.3181openalex-snapshot
Multiple Identifications in Multi-Armed Bandits
inferredcites →https://doi.org/10.48550/arxiv.1605.08671openalex-snapshot
An optimal algorithm for the Thresholding Bandit Problem
inferredcites →https://doi.org/10.5555/1248547.1248586openalex-snapshot
Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4599462openalex-extracted
A Large Deviation Algorithm for the Thresholding Bandit Problem
inferred← citeshttps://doi.org/10.1007/978-3-031-36938-4_5openalex-snapshot
Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy
inferred← citeshttps://doi.org/10.1007/s00186-022-00769-xopenalex-snapshot
A PAC algorithm in relative precision for bandit problem with costly sampling
inferred← citeshttps://doi.org/10.1007/s10994-019-05784-4openalex-snapshot
Good arm identification via bandit feedback
inferred← citeshttps://doi.org/10.1609/aaai.v32i1.12110openalex-snapshot
Efficient-UCBV: An Almost Optimal Algorithm Using Variance Estimates
inferred← citeshttps://doi.org/10.26599/bdma.2025.9020028openalex-snapshot
Large Deviation Algorithms for Thresholding Bandit Problem
inferred← citeshttps://doi.org/10.48550/arxiv.1905.09190openalex-snapshot
Thresholding Graph Bandits with GrAPL
inferred← citeshttps://doi.org/10.48550/arxiv.1906.06594openalex-snapshot
The True Sample Complexity of Identifying Good Arms
inferred← citeshttps://doi.org/10.70675/17272c10zb624z400ezac8cz9af047c3b80eopenalex-snapshot
Approximation and model reduction for partial differential equations with probabilistic interpretation
inferred← citeshttps://doi.org/10.70675/65495d74zd2c7z43eaza609zca30a7a66ef0openalex-snapshot
Toward realistic reinforcement learning
inferred← citeshttps://doi.org/10.70675/7b92f6e7z5ec9z4ffaz917bz40903858426bopenalex-snapshot
Apprentissage automatique séquentiel pour les systèmes éducatifs intelligents
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