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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 911–920 of 1262 papers

TitleStatusHype
SPRT-based Efficient Best Arm Identification in Stochastic Bandits—0
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits—0
Stability Enforced Bandit Algorithms for Channel Selection in Remote State Estimation of Gauss-Markov Processes—0
Stabilizing the Kumaraswamy Distribution—0
Stateful Offline Contextual Policy Evaluation and Learning—0
Statistical Inference with M-Estimators on Adaptively Collected Data—0
Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits—0
Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits—0
Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond—0
Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling—0
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Benchmark Results

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