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 26–50 of 1262 papers

TitleStatusHype
Performance-bounded Online Ensemble Learning Method Based on Multi-armed bandits and Its Applications in Real-time Safety AssessmentCode1
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed BanditsCode1
Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless BanditsCode1
Hierarchical Adaptive Contextual Bandits for Resource Constraint based RecommendationCode1
A Modern Introduction to Online LearningCode1
Anytime-valid off-policy inference for contextual banditsCode1
A unifying framework for generalised Bayesian online learning in non-stationary environmentsCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed BanditsCode1
EE-Net: Exploitation-Exploration Neural Networks in Contextual BanditsCode1
Competing for Shareable Arms in Multi-Player Multi-Armed BanditsCode1
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions ModelingCode1
Adapting to Delays and Data in Adversarial Multi-Armed Bandits—0
A Classification View on Meta Learning Bandits—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Adapting Bandit Algorithms for Settings with Sequentially Available Arms—0
AdaptEx: A Self-Service Contextual Bandit Platform—0
Achieving User-Side Fairness in Contextual Bandits—0
α-Fair Contextual Bandits—0
AdaLinUCB: Opportunistic Learning for Contextual Bandits—0
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits—0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
Active Search for Sparse Signals with Region Sensing—0
A Batch Sequential Halving Algorithm without Performance Degradation—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
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Benchmark Results

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