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 1181–1190 of 1262 papers

TitleStatusHype
Making Contextual Decisions with Low Technical Debt—0
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits—0
Contextual Bandits with Latent Confounders: An NMF Approach—0
Open Problem: Best Arm Identification: Almost Instance-Wise Optimality and the Gap Entropy Conjecture—0
Fairness in Learning: Classic and Contextual Bandits—0
Graph Clustering Bandits for Recommendation—0
Stochastic Contextual Bandits with Known Reward Functions—0
Latent Contextual Bandits and their Application to Personalized Recommendations for New Users—0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
PAC Reinforcement Learning with Rich Observations—0
Show:102550
← PrevPage 119 of 127Next →

Benchmark Results

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