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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 601–610 of 1262 papers

TitleStatusHype
Multi-armed Bandits for Link Configuration in Millimeter-wave Networks—0
Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts—0
Adaptive Experimentation with Delayed Binary FeedbackCode0
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health—0
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems—0
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound FrameworkCode0
Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo RecommendationsCode0
Neural Collaborative Filtering Bandits via Meta Learning—0
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms—0
Networked Restless Multi-Armed Bandits for Mobile Interventions—0
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Benchmark Results

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