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 771–780 of 1262 papers

TitleStatusHype
Player Modeling via Multi-Armed Bandits—0
Policy Gradients for Contextual Recommendations—0
Practical Algorithms for Best-K Identification in Multi-Armed Bandits—0
Practical Contextual Bandits with Regression Oracles—0
Preference-based Online Learning with Dueling Bandits: A Survey—0
Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms—0
Privacy Amplification via Shuffling for Linear Contextual Bandits—0
Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits—0
Privacy-Preserving Multi-Party Contextual Bandits—0
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs—0
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Benchmark Results

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