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 11–20 of 1262 papers

TitleStatusHype
Competing for Shareable Arms in Multi-Player Multi-Armed BanditsCode1
Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed banditsCode1
Neural Exploitation and Exploration of Contextual BanditsCode1
Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless BanditsCode1
Anytime-valid off-policy inference for contextual banditsCode1
Multi-agent Dynamic Algorithm ConfigurationCode1
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence ModelingCode1
Langevin Monte Carlo for Contextual BanditsCode1
SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge EnvironmentsCode1
Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent SurfacesCode1
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Benchmark Results

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