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 351–360 of 1262 papers

TitleStatusHype
Best Arm Identification in Restless Markov Multi-Armed Bandits—0
Best arm identification in multi-armed bandits with delayed feedback—0
Best Arm Identification in Linked Bandits—0
Best-Arm Identification in Correlated Multi-Armed Bandits—0
An Efficient Algorithm for Deep Stochastic Contextual Bandits—0
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds—0
Active Reinforcement Learning: Observing Rewards at a Cost—0
Quantile Multi-Armed Bandits: Optimal Best-Arm Identification and a Differentially Private Scheme—0
Efficient Prompt Optimization Through the Lens of Best Arm Identification—0
An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives—0
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Benchmark Results

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