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 161–170 of 1262 papers

TitleStatusHype
Bandits with Temporal Stochastic Constraints—0
Banker Online Mirror Descent—0
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning—0
Batched Bandits with Crowd Externalities—0
Batched Coarse Ranking in Multi-Armed Bandits—0
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits—0
Regret Bounds for Batched Bandits—0
Batched Nonparametric Bandits via k-Nearest Neighbor UCB—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
A Gang of Bandits—0
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Benchmark Results

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