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 451–460 of 1262 papers

TitleStatusHype
Evolution of Information in Interactive Decision Making: A Case Study for Multi-Armed Bandits—0
EVOLvE: Evaluating and Optimizing LLMs For Exploration—0
Expanding on Repeated Consumer Search Using Multi-Armed Bandits and Secretaries—0
Expected Improvement-based Contextual Bandits—0
Explicit Best Arm Identification in Linear Bandits Using No-Regret Learners—0
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment—0
Exploration Potential—0
Exploration Through Bias: Revisiting Biased Maximum Likelihood Estimation in Stochastic Multi-Armed Bandits—0
Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits—0
Deep Contextual Multi-armed Bandits—0
Show:102550
← PrevPage 46 of 127Next →

Benchmark Results

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