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 631–640 of 1262 papers

TitleStatusHype
Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks—0
Fairness in Learning: Classic and Contextual Bandits—0
Fairness of Exposure in Stochastic Bandits—0
Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits—0
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
Faster Maximum Inner Product Search in High Dimensions—0
Faster Q-Learning Algorithms for Restless Bandits—0
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise—0
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
Federated Linear Bandits with Finite Adversarial Actions—0
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Benchmark Results

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