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

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Showing 121–130 of 1262 papers

TitleStatusHype
Causal Contextual Bandits with Adaptive ContextCode0
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded RewardsCode0
A Survey of Online Experiment Design with the Stochastic Multi-Armed BanditCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
A Survey on Contextual Multi-armed BanditsCode0
Learning Structural Weight Uncertainty for Sequential Decision-MakingCode0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
Causally Abstracted Multi-armed BanditsCode0
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic EnvironmentsCode0
A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed BanditsCode0
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Benchmark Results

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