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 181–190 of 1262 papers

TitleStatusHype
Latent Bottlenecked Attentive Neural ProcessesCode0
Learning Contextual Bandits in a Non-stationary EnvironmentCode0
Linear Contextual Bandits with Hybrid Payoff: RevisitedCode0
Locally Differentially Private (Contextual) Bandits LearningCode0
Confidence Intervals for Policy Evaluation in Adaptive ExperimentsCode0
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous ActionsCode0
Adaptive Linear Estimating EquationsCode0
Marginal Density Ratio for Off-Policy Evaluation in Contextual BanditsCode0
Best Arm Identification with Fixed Budget: A Large Deviation PerspectiveCode0
Decentralized Cooperative Stochastic BanditsCode0
Show:102550
← PrevPage 19 of 127Next →

Benchmark Results

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