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 501–510 of 1262 papers

TitleStatusHype
Finite-Horizon Single-Pull Restless Bandits: An Efficient Index Policy For Scarce Resource Allocation—0
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy—0
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health—0
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation—0
Batched Thompson Sampling for Multi-Armed Bandits—0
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits—0
Fixed-Budget Best-Arm Identification in Structured Bandits—0
FLASH: Federated Learning Across Simultaneous Heterogeneities—0
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles—0
Decision Automation for Electric Power Network Recovery—0
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Benchmark Results

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