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 981–990 of 1262 papers

TitleStatusHype
A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits—0
Nonparametric Contextual Bandits in an Unknown Metric Space—0
Doubly-Robust Lasso BanditCode0
Scaling Multi-Armed Bandit Algorithms—0
Doubly robust off-policy evaluation with shrinkage—0
Parameterized Exploration—0
Productization Challenges of Contextual Multi-Armed Bandits—0
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits—0
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits—0
Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates—0
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Benchmark Results

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