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 861–870 of 1262 papers

TitleStatusHype
Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms—0
Multiplier Bootstrap-based Exploration—0
MultiScale Contextual Bandits for Long Term Objectives—0
Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits—0
Multi-Task Learning for Contextual Bandits—0
Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access—0
Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access—0
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users—0
Nearest Neighbor Search Under Uncertainty—0
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits—0
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Benchmark Results

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