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 1251–1262 of 1262 papers

TitleStatusHype
Contextual Bandits with Cross-learning—0
Contextual Bandits with Knapsacks for a Conversion Model—0
Contextual Bandits with Latent Confounders: An NMF Approach—0
Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks—0
Contextual Bandits with Online Neural Regression—0
Contextual Bandits with Random Projection—0
Contextual Bandits with Side-Observations—0
Contextual Bandits with Similarity Information—0
Contextual Bandits with Sparse Data in Web setting—0
Contextual Bandits with Stage-wise Constraints—0
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Show:102550
← PrevPage 26 of 26Next →

Benchmark Results

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