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 941–950 of 1262 papers

TitleStatusHype
Fair Contextual Multi-Armed Bandits: Theory and Experiments—0
Sublinear Optimal Policy Value Estimation in Contextual Bandits—0
Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric—0
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits—0
Thompson Sampling for Multinomial Logit Contextual BanditsCode0
Offline Contextual Bandits with High Probability Fairness GuaranteesCode0
Learning in Generalized Linear Contextual Bandits with Stochastic Delays—0
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making—0
Contextual Combinatorial Conservative Bandits—0
Automatic Ensemble Learning for Online Influence Maximization—0
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Benchmark Results

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