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 1231–1240 of 1262 papers

TitleStatusHype
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits—0
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation—0
Distributed Exploration in Multi-Armed Bandits—0
Generalized Thompson Sampling for Contextual Bandits—0
Multi-Armed Bandits for Intelligent Tutoring Systems—0
Sequential Monte Carlo Bandits—0
Finite-Time Analysis of Kernelised Contextual Bandits—0
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens—0
Distributed Online Learning via Cooperative Contextual Bandits—0
Modeling Human Decision-making in Generalized Gaussian Multi-armed Bandits—0
Show:102550
← PrevPage 124 of 127Next →

Benchmark Results

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