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 531–540 of 1262 papers

TitleStatusHype
Conformal Off-Policy Prediction in Contextual Bandits—0
Neural Bandit with Arm Group Graph—0
Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits—0
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
Group Meritocratic Fairness in Linear Contextual BanditsCode0
Robust Pareto Set Identification with Contaminated Bandit Feedback—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
Contextual Bandits with Knapsacks for a Conversion Model—0
Provably and Practically Efficient Neural Contextual Bandits—0
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Benchmark Results

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