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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 601–625 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
Top-K Ranking Deep Contextual Bandits for Information Selection Systems—0
Networked Restless Multi-Armed Bandits for Mobile Interventions—0
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits—0
Learning Neural Contextual Bandits Through Perturbed Rewards—0
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search—0
Semantic Parsing for Planning Goals as Constrained Combinatorial Contextual Bandits—0
Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)—0
Modelling Cournot Games as Multi-agent Multi-armed Bandits—0
Off-Policy Evaluation Using Information Borrowing and Context-Based SwitchingCode0
Safe Linear Leveling Bandits—0
Stochastic differential equations for limiting description of UCB rule for Gaussian multi-armed bandits—0
Privacy Amplification via Shuffling for Linear Contextual Bandits—0
Efficient Action Poisoning Attacks on Linear Contextual Bandits—0
Best Arm Identification under Additive Transfer Bandits—0
Contextual Bandit Applications in Customer Support Bot—0
On Submodular Contextual Bandits—0
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Benchmark Results

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