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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 1051–1100 of 1262 papers

TitleStatusHype
Towards Fundamental Limits of Multi-armed Bandits with Random Walk Feedback—0
Rarely-switching linear bandits: optimization of causal effects for the real world—0
Rate-Constrained Remote Contextual Bandits—0
Reciprocal Learning—0
Recommenadation aided Caching using Combinatorial Multi-armed Bandits—0
Budgeted Multi-Armed Bandits with Asymmetric Confidence IntervalsCode0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
Incorporating Multi-armed Bandit with Local Search for MaxSATCode0
VITS : Variational Inference Thompson Sampling for contextual banditsCode0
Causal Contextual Bandits with Adaptive ContextCode0
Efficient Explorative Key-term Selection Strategies for Conversational Contextual BanditsCode0
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic EnvironmentsCode0
Causally Abstracted Multi-armed BanditsCode0
Censored Semi-Bandits: A Framework for Resource Allocation with Censored FeedbackCode0
Online Learning for Function Placement in Serverless ComputingCode0
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO AlgorithmCode0
Optimal Contextual Bandits with Knapsacks under Realizability via Regression OraclesCode0
Efficient Kernel UCB for Contextual BanditsCode0
Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic maskingCode0
Multi-Armed Bandits in Brain-Computer InterfacesCode0
Off-Policy Evaluation of Slate Bandit Policies via Optimizing AbstractionCode0
Off-Policy Evaluation Using Information Borrowing and Context-Based SwitchingCode0
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
Combinatorial Bandits under Strategic ManipulationsCode0
Adapting multi-armed bandits policies to contextual bandits scenariosCode0
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