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 971–980 of 1262 papers

TitleStatusHype
Social Learning in Multi Agent Multi Armed Bandits—0
Decision Automation for Electric Power Network Recovery—0
An Optimal Algorithm for Multiplayer Multi-Armed Bandits—0
NeuralUCB: Contextual Bandits with Neural Network-Based Exploration—0
Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood MatchingCode0
Learning Effective Exploration Strategies For Contextual Bandits—0
Practical Calculation of Gittins Indices for Multi-armed BanditsCode0
AutoML for Contextual Bandits—0
Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret RegimesCode0
Censored Semi-Bandits: A Framework for Resource Allocation with Censored FeedbackCode0
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Benchmark Results

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