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 171–180 of 1262 papers

TitleStatusHype
Combinatorial Bandits under Strategic ManipulationsCode0
Budgeted Multi-Armed Bandits with Asymmetric Confidence IntervalsCode0
Incorporating Multi-armed Bandit with Local Search for MaxSATCode0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
Invariant Policy Learning: A Causal PerspectiveCode0
Bayesian Design Principles for Frequentist Sequential LearningCode0
Bayesian Optimisation over Multiple Continuous and Categorical InputsCode0
IRL for Restless Multi-Armed Bandits with Applications in Maternal and Child HealthCode0
Best Arm Identification with Fixed Budget: A Large Deviation PerspectiveCode0
Model selection for contextual banditsCode0
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Benchmark Results

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