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 361–370 of 1262 papers

TitleStatusHype
Differentially Private Kernelized Contextual Bandits—0
Differentially Private Multi-Armed Bandits in the Shuffle Model—0
Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?—0
Diffusion Approximations for Thompson Sampling—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Diminishing Exploration: A Minimalist Approach to Piecewise Stationary Multi-Armed Bandits—0
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option—0
Discrete Choice Multi-Armed Bandits—0
Disentangling Exploration from Exploitation—0
Adapting to Misspecification in Contextual Bandits—0
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Benchmark Results

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