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 981–990 of 1262 papers

TitleStatusHype
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits—0
Optimal Best-Arm Identification under Fixed Confidence with Multiple Optima—0
Optimal cross-learning for contextual bandits with unknown context distributions—0
Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity—0
Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks—0
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective—0
Optimal Multi-Objective Best Arm Identification with Fixed Confidence—0
Optimal No-regret Learning in Repeated First-price Auctions—0
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits—0
Optimal Streaming Algorithms for Multi-Armed Bandits—0
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Benchmark Results

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