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 751–760 of 1262 papers

TitleStatusHype
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
Encrypted Linear Contextual Bandit—0
Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks—0
Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems—0
Nearest Neighbor Search Under Uncertainty—0
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes—0
Fairness of Exposure in Stochastic Bandits—0
Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles—0
Local Clustering in Contextual Multi-Armed Bandits—0
Federated Multi-armed Bandits with PersonalizationCode0
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Benchmark Results

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