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 261–270 of 1262 papers

TitleStatusHype
Adversarial Attacks on Linear Contextual Bandits—0
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation—0
Combinatorial Pure Exploration of Multi-Armed Bandits—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
Combinatorial Network Optimization with Unknown Variables: Multi-Armed Bandits with Linear Rewards—0
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond—0
A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits—0
Adversarial Attacks on Cooperative Multi-agent Bandits—0
A Classification View on Meta Learning Bandits—0
Combinatorial Multi-Armed Bandits with Filtered Feedback—0
Show:102550
← PrevPage 27 of 127Next →

Benchmark Results

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