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 611–620 of 1262 papers

TitleStatusHype
Efficient Kernel UCB for Contextual BanditsCode0
Shuffle Private Linear Contextual Bandits—0
Remote Contextual Bandits—0
Settling the Communication Complexity for Distributed Offline Reinforcement Learning—0
Smoothed Online Learning is as Easy as Statistical Learning—0
Budgeted Combinatorial Multi-Armed Bandits—0
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits—0
Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts—0
Adaptive Experimentation with Delayed Binary FeedbackCode0
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health—0
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Benchmark Results

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