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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 601–625 of 1262 papers

TitleStatusHype
Approximate Function Evaluation via Multi-Armed Bandits—0
Reinforced Meta Active Learning—0
Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits—0
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits—0
Restless Multi-Armed Bandits under Exogenous Global Markov Process—0
Federated Online Sparse Decision Making—0
Truncated LinUCB for Stochastic Linear BanditsCode0
The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication—0
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits—0
Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences—0
Shuffle Private Linear Contextual Bandits—0
Efficient Kernel UCB for Contextual BanditsCode0
Remote Contextual Bandits—0
Settling the Communication Complexity for Distributed Offline Reinforcement Learning—0
Smoothed Online Learning is as Easy as Statistical Learning—0
Budgeted Combinatorial Multi-Armed Bandits—0
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits—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
Multi-armed Bandits for Link Configuration in Millimeter-wave Networks—0
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems—0
Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo RecommendationsCode0
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound FrameworkCode0
Neural Collaborative Filtering Bandits via Meta Learning—0
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Benchmark Results

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