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 41–50 of 1262 papers

TitleStatusHype
Adapting Bandit Algorithms for Settings with Sequentially Available Arms—0
AdaptEx: A Self-Service Contextual Bandit Platform—0
Achieving User-Side Fairness in Contextual Bandits—0
α-Fair Contextual Bandits—0
AdaLinUCB: Opportunistic Learning for Contextual Bandits—0
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits—0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
Active Search for Sparse Signals with Region Sensing—0
A Batch Sequential Halving Algorithm without Performance Degradation—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
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Benchmark Results

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