SOTAVerified

Multi-Armed Bandits

Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.

( Image credit: Microsoft Research )

Papers

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Showing 701–710 of 1262 papers

TitleStatusHype
High-dimensional Nonparametric Contextual Bandit Problem—0
High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions—0
Encrypted Linear Contextual Bandit—0
Honor Among Bandits: No-Regret Learning for Online Fair Division—0
Horde of Bandits using Gaussian Markov Random Fields—0
How Does Variance Shape the Regret in Contextual Bandits?—0
Human-AI Learning Performance in Multi-Armed Bandits—0
Hypothesis Transfer in Bandits by Weighted Models—0
Identifiable latent bandits: Combining observational data and exploration for personalized healthcare—0
Imitation-Regularized Offline Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NeuralLinear FullPosterior-MRCumulative regret1.92—Unverified
2Linear FullPosterior-MRCumulative regret1.82—Unverified