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 1061–1070 of 1262 papers

TitleStatusHype
Adversarial Attacks on Cooperative Multi-agent Bandits—0
Adversarial Attacks on Linear Contextual Bandits—0
Adversarial Bandits with Knapsacks—0
Adversarial Contextual Bandits Go Kernelized—0
Adversarial Linear Contextual Bandits with Graph-Structured Side Observations—0
α-Fair Contextual Bandits—0
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—0
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Benchmark Results

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