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 591–600 of 1262 papers

TitleStatusHype
Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences—0
Off-Policy Evaluation for Large Action Spaces via EmbeddingsCode2
Shuffle Private Linear Contextual Bandits—0
Efficient Kernel UCB for Contextual BanditsCode0
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
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior ModelCode2
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Benchmark Results

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