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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 1076–1100 of 1262 papers

TitleStatusHype
Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit ProblemsCode0
Maximizing and Satisficing in Multi-armed Bandits with Graph InformationCode0
Combinatorial Multi-armed Bandits for Resource AllocationCode0
Empirical Likelihood for Contextual BanditsCode0
Online SuBmodular + SuPermodular (BP) Maximization with Bandit FeedbackCode0
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement LearningCode0
Introduction to Multi-Armed BanditsCode0
Invariant Policy Learning: A Causal PerspectiveCode0
Equal Opportunity in Online Classification with Partial FeedbackCode0
Inverse Contextual Bandits: Learning How Behavior Evolves over TimeCode0
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed BanditsCode0
Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous AgentsCode0
Information-Directed Selection for Top-Two AlgorithmsCode0
Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex NetworksCode0
IRL for Restless Multi-Armed Bandits with Applications in Maternal and Child HealthCode0
Estimation of Warfarin Dosage with Reinforcement LearningCode0
Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo RecommendationsCode0
Model selection for contextual banditsCode0
Best Arm Identification with Fixed Budget: A Large Deviation PerspectiveCode0
Evolutionary Multi-Armed Bandits with Genetic Thompson SamplingCode0
Optimal Learning for Structured BanditsCode0
Conditionally Risk-Averse Contextual BanditsCode0
Tight Regret Bounds for Single-pass Streaming Multi-armed BanditsCode0
Confidence Intervals for Policy Evaluation in Adaptive ExperimentsCode0
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance WeightingCode0
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Benchmark Results

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