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 1–50 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
Federated Multi-Armed BanditsCode1
An empirical evaluation of active inference in multi-armed banditsCode1
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed BanditsCode1
Neural Thompson SamplingCode1
Carousel Personalization in Music Streaming Apps with Contextual BanditsCode1
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed BanditsCode1
Efficient Contextual Bandits with Continuous ActionsCode1
Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RLCode1
Hierarchical Adaptive Contextual Bandits for Resource Constraint based RecommendationCode1
A Modern Introduction to Online LearningCode1
Multiplayer Multi-armed Bandits for Optimal Assignment in Heterogeneous NetworksCode1
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions ModelingCode1
Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards—0
A General Framework for Off-Policy Learning with Partially-Observed Reward—0
Adaptive Data Augmentation for Thompson Sampling—0
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic EnvironmentsCode0
Stochastic Multi-Objective Multi-Armed Bandits: Regret Definition and Algorithm—0
Collaborative Min-Max Regret in Grouped Multi-Armed Bandits—0
Meet Me at the Arm: The Cooperative Multi-Armed Bandits Problem with Shareable Arms—0
Improved Regret Bounds for Linear Bandits with Heavy-Tailed Rewards—0
From Theory to Practice with RAVEN-UCB: Addressing Non-Stationarity in Multi-Armed Bandits through Variance AdaptationCode0
VirnyFlow: A Design Space for Responsible Model DevelopmentCode0
Quick-Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms—0
COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents—0
A Reinforcement-Learning-Enhanced LLM Framework for Automated A/B Testing in Personalized Marketing—0
Show:102550
← PrevPage 1 of 26Next →

Benchmark Results

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