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 401–410 of 1262 papers

TitleStatusHype
Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits—0
Dynamic Global Sensitivity for Differentially Private Contextual Bandits—0
Dynamic pricing and assortment under a contextual MNL demand—0
Dynamic Pricing with Limited Supply—0
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce—0
Early Stopping in Contextual Bandits and Inferences—0
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
EduQate: Generating Adaptive Curricula through RMABs in Education Settings—0
Adapting to Misspecification in Contextual Bandits—0
Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts—0
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Benchmark Results

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