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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 741–750 of 1262 papers

TitleStatusHype
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
Optimistic Information Directed Sampling—0
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits—0
Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics—0
Optimizing Sharpe Ratio: Risk-Adjusted Decision-Making in Multi-Armed Bandits—0
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Benchmark Results

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