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 481–490 of 1262 papers

TitleStatusHype
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
Federated Linear Bandits with Finite Adversarial Actions—0
Federated Linear Contextual Bandits—0
Federated Linear Contextual Bandits with Heterogeneous Clients—0
Federated Linear Contextual Bandits with User-level Differential Privacy—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy—0
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health—0
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Benchmark Results

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