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 131–140 of 1262 papers

TitleStatusHype
Pairwise Elimination with Instance-Dependent Guarantees for Bandits with Cost Subsidy—0
Neural Risk-sensitive Satisficing in Contextual Bandits—0
Differentially Private Kernelized Contextual Bandits—0
Finite-Horizon Single-Pull Restless Bandits: An Efficient Index Policy For Scarce Resource Allocation—0
On The Statistical Complexity of Offline Decision-Making—0
An Instrumental Value for Data Production and its Application to Data Pricing—0
A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON—0
Lagrangian Index Policy for Restless Bandits with Average Reward—0
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization—0
An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints—0
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Benchmark Results

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