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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 901–910 of 1262 papers

TitleStatusHype
Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback—0
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without—0
No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization—0
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization—0
Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration—0
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search—0
Offline Clustering of Linear Bandits: Unlocking the Power of Clusters in Data-Limited Environments—0
Offline Contextual Bandits for Wireless Network Optimization—0
Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation—0
Offline Learning for Combinatorial Multi-armed Bandits—0
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Benchmark Results

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