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 1111–1120 of 1262 papers

TitleStatusHype
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson SamplingCode0
Contextual Bandits with Stochastic ExpertsCode0
Regional Multi-Armed Bandits—0
Online Learning with an Unknown Fairness Metric—0
Multi-Armed Bandits on Partially Revealed Unit Interval Graphs—0
Policy Gradients for Contextual Recommendations—0
More Robust Doubly Robust Off-policy Evaluation—0
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits—0
Nonparametric Stochastic Contextual Bandits—0
Contextual memory bandit for pro-active dialog engagement—0
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Benchmark Results

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