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 311–320 of 1262 papers

TitleStatusHype
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens—0
Budgeted Recommendation with Delayed Feedback—0
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits—0
Budgeted Combinatorial Multi-Armed Bandits—0
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays—0
Adaptive, Robust and Scalable Bayesian Filtering for Online Learning—0
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits—0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
Budget-Constrained Multi-Armed Bandits with Multiple Plays—0
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
Show:102550
← PrevPage 32 of 127Next →

Benchmark Results

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