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 381–390 of 1262 papers

TitleStatusHype
Sequential Best-Arm Identification with Application to Brain-Computer Interface—0
Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed banditsCode1
Efficient Training of Multi-task Combinarotial Neural Solver with Multi-armed Bandits—0
Neural Exploitation and Exploration of Contextual BanditsCode1
Reward Teaching for Federated Multi-armed Bandits—0
Stochastic Contextual Bandits with Graph-based Contexts—0
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits—0
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded RewardsCode0
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards—0
Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement LearningCode0
Show:102550
← PrevPage 39 of 127Next →

Benchmark Results

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