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 26–50 of 1262 papers

TitleStatusHype
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
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Benchmark Results

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