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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 1–10 of 1262 papers

TitleStatusHype
Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards—0
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic EnvironmentsCode0
A General Framework for Off-Policy Learning with Partially-Observed Reward—0
Adaptive Data Augmentation for Thompson Sampling—0
Stochastic Multi-Objective Multi-Armed Bandits: Regret Definition and Algorithm—0
Collaborative Min-Max Regret in Grouped Multi-Armed Bandits—0
Meet Me at the Arm: The Cooperative Multi-Armed Bandits Problem with Shareable Arms—0
Improved Regret Bounds for Linear Bandits with Heavy-Tailed Rewards—0
From Theory to Practice with RAVEN-UCB: Addressing Non-Stationarity in Multi-Armed Bandits through Variance AdaptationCode0
VirnyFlow: A Design Space for Responsible Model DevelopmentCode0
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Benchmark Results

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