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 1021–1030 of 1262 papers

TitleStatusHype
A Bandit Approach to Sequential Experimental Design with False Discovery Control—0
A Batch Sequential Halving Algorithm without Performance Degradation—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond—0
Access Probability Optimization in RACH: A Multi-Armed Bandits Approach—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
A Central Limit Theorem, Loss Aversion and Multi-Armed Bandits—0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
Achieving User-Side Fairness in Contextual Bandits—0
A Classification View on Meta Learning Bandits—0
Show:102550
← PrevPage 103 of 127Next →

Benchmark Results

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