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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 381–390 of 1262 papers

TitleStatusHype
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits—0
Distributionally Robust Batch Contextual Bandits—0
Distribution-dependent and Time-uniform Bounds for Piecewise i.i.d Bandits—0
Distribution-Dependent Rates for Multi-Distribution Learning—0
Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience—0
Diversity-Based Recruitment in Crowdsensing By Combinatorial Multi-Armed Bandits—0
Diversity-Driven Selection of Exploration Strategies in Multi-Armed Bandits—0
DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback—0
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits—0
Adapting to Misspecification in Contextual Bandits—0
Show:102550
← PrevPage 39 of 127Next →

Benchmark Results

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