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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 141–150 of 1262 papers

TitleStatusHype
DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback—0
High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions—0
Online Posterior Sampling with a Diffusion Prior—0
Minimax-optimal trust-aware multi-armed bandits—0
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs—0
On Lai's Upper Confidence Bound in Multi-Armed Bandits—0
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed BanditsCode1
Stabilizing the Kumaraswamy Distribution—0
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits—0
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Benchmark Results

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