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 171–180 of 1262 papers

TitleStatusHype
Batched Thompson Sampling—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
Towards Bayesian Data Selection—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
An Analysis of Reinforcement Learning for Malaria Control—0
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits—0
BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes—0
Beam Learning -- Using Machine Learning for Finding Beam Directions—0
Balanced off-policy evaluation in general action spaces—0
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Benchmark Results

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