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 241–250 of 1262 papers

TitleStatusHype
Top-k eXtreme Contextual Bandits with Arm HierarchyCode0
Towards the D-Optimal Online Experiment Design for Recommender SelectionCode0
Networked Restless Bandits with Positive ExternalitiesCode0
Two-Stage Neural Contextual Bandits for Personalised News RecommendationCode0
Asymptotically Best Causal Effect Identification with Multi-Armed Bandits—0
Adversarial Contextual Bandits Go Kernelized—0
Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensity—0
A Survey of Risk-Aware Multi-Armed Bandits—0
Communication Efficient Distributed Learning for Kernelized Contextual Bandits—0
Adversarial Bandits with Knapsacks—0
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Benchmark Results

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