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 391–400 of 1262 papers

TitleStatusHype
Optimal Activation of Halting Multi-Armed Bandit Models—0
A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed BanditsCode0
Learning Personalized Decision Support Policies—0
SmartChoices: Augmenting Software with Learned Implementations—0
BanditQ: Fair Bandits with Guaranteed Rewards—0
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion—0
Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms—0
Federated Learning for Heterogeneous Bandits with Unobserved Contexts—0
Adaptive Endpointing with Deep Contextual Multi-armed Bandits—0
An Empirical Evaluation of Federated Contextual Bandit Algorithms—0
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Benchmark Results

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