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 31–40 of 1262 papers

TitleStatusHype
EE-Net: Exploitation-Exploration Neural Networks in Contextual BanditsCode1
An empirical evaluation of active inference in multi-armed banditsCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed BanditsCode1
Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless BanditsCode1
Competing for Shareable Arms in Multi-Player Multi-Armed BanditsCode1
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions ModelingCode1
Adapting to Delays and Data in Adversarial Multi-Armed Bandits—0
A Classification View on Meta Learning Bandits—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
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Benchmark Results

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