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 991–1000 of 1262 papers

TitleStatusHype
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
Bayesian Optimisation over Multiple Continuous and Categorical InputsCode0
Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules—0
Online Allocation and Pricing: Constant Regret via Bellman Inequalities—0
Competing Bandits in Matching Markets—0
Bootstrapping Upper Confidence Bound—0
Beam Learning -- Using Machine Learning for Finding Beam Directions—0
Stochastic Neural Network with Kronecker Flow—0
Balanced off-policy evaluation in general action spaces—0
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Benchmark Results

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