SOTAVerified

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 1–25 of 1262 papers

TitleStatusHype
Hypothesis Generation with Large Language ModelsCode2
Off-Policy Evaluation for Large Action Spaces via EmbeddingsCode2
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior ModelCode2
Performance-bounded Online Ensemble Learning Method Based on Multi-armed bandits and Its Applications in Real-time Safety AssessmentCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
A unifying framework for generalised Bayesian online learning in non-stationary environmentsCode1
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed BanditsCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
In-Context Reinforcement Learning for Variable Action SpacesCode1
Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital HealthCode1
Competing for Shareable Arms in Multi-Player Multi-Armed BanditsCode1
Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed banditsCode1
Neural Exploitation and Exploration of Contextual BanditsCode1
Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless BanditsCode1
Anytime-valid off-policy inference for contextual banditsCode1
Multi-agent Dynamic Algorithm ConfigurationCode1
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence ModelingCode1
Langevin Monte Carlo for Contextual BanditsCode1
SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge EnvironmentsCode1
Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent SurfacesCode1
Offline Neural Contextual Bandits: Pessimism, Optimization and GeneralizationCode1
EE-Net: Exploitation-Exploration Neural Networks in Contextual BanditsCode1
Generalized Linear Bandits with Local Differential PrivacyCode1
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual BanditsCode1
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep NetworksCode1
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Benchmark Results

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