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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 1–50 of 1262 papers

TitleStatusHype
Off-Policy Evaluation for Large Action Spaces via EmbeddingsCode2
Hypothesis Generation with Large Language ModelsCode2
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior ModelCode2
Generalized Linear Bandits with Local Differential PrivacyCode1
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed BanditsCode1
In-Context Reinforcement Learning for Variable Action SpacesCode1
Performance-bounded Online Ensemble Learning Method Based on Multi-armed bandits and Its Applications in Real-time Safety AssessmentCode1
Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital HealthCode1
Neural Exploitation and Exploration of Contextual BanditsCode1
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual BanditsCode1
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence ModelingCode1
A unifying framework for generalised Bayesian online learning in non-stationary environmentsCode1
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed BanditsCode1
Anytime-valid off-policy inference for contextual banditsCode1
EE-Net: Exploitation-Exploration Neural Networks in Contextual BanditsCode1
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions ModelingCode1
An empirical evaluation of active inference in multi-armed banditsCode1
Hierarchical Adaptive Contextual Bandits for Resource Constraint based RecommendationCode1
Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless BanditsCode1
Multi-agent Dynamic Algorithm ConfigurationCode1
Offline Neural Contextual Bandits: Pessimism, Optimization and GeneralizationCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge EnvironmentsCode1
Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RLCode1
Competing for Shareable Arms in Multi-Player Multi-Armed BanditsCode1
Carousel Personalization in Music Streaming Apps with Contextual BanditsCode1
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep NetworksCode1
Neural Thompson SamplingCode1
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed BanditsCode1
Langevin Monte Carlo for Contextual BanditsCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
Efficient Contextual Bandits with Continuous ActionsCode1
Multiplayer Multi-armed Bandits for Optimal Assignment in Heterogeneous NetworksCode1
Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed banditsCode1
Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent SurfacesCode1
A Modern Introduction to Online LearningCode1
Federated Multi-Armed BanditsCode1
Combinatorial Multi-armed Bandits for Resource AllocationCode0
Adapting multi-armed bandits policies to contextual bandits scenariosCode0
Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous AgentsCode0
Censored Semi-Bandits: A Framework for Resource Allocation with Censored FeedbackCode0
Causally Abstracted Multi-armed BanditsCode0
Combinatorial Bandits under Strategic ManipulationsCode0
Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex NetworksCode0
Budgeted Multi-Armed Bandits with Asymmetric Confidence IntervalsCode0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO AlgorithmCode0
Best Arm Identification with Fixed Budget: A Large Deviation PerspectiveCode0
Bandit-Based Monte Carlo Optimization for Nearest NeighborsCode0
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewardsCode0
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Benchmark Results

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