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 291–300 of 1262 papers

TitleStatusHype
Contextual Bandits with Side-Observations—0
Contextual Bandits with Similarity Information—0
BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System—0
Contextual Bandits with Sparse Data in Web setting—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
Contextual Bandits with Stage-wise Constraints—0
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Contextual Causal Bayesian Optimisation—0
Context-Aware Bandits—0
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Benchmark Results

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