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 1161–1170 of 1262 papers

TitleStatusHype
Towards Bayesian Data Selection—0
Bayesian decision-making under misspecified priors with applications to meta-learning—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
Be Greedy in Multi-Armed Bandits—0
Efficient Prompt Optimization Through the Lens of Best Arm Identification—0
Quantile Multi-Armed Bandits: Optimal Best-Arm Identification and a Differentially Private Scheme—0
Best-Arm Identification in Correlated Multi-Armed Bandits—0
Best Arm Identification in Linked Bandits—0
Best arm identification in multi-armed bandits with delayed feedback—0
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Benchmark Results

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