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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 376–400 of 1262 papers

TitleStatusHype
Competing for Shareable Arms in Multi-Player Multi-Armed BanditsCode1
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits—0
Contextual Bandits with Budgeted Information Reveal—0
Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness—0
Meta-in-context learning in large language modelsCode0
Sequential Best-Arm Identification with Application to Brain-Computer Interface—0
Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed banditsCode1
Efficient Training of Multi-task Combinarotial Neural Solver with Multi-armed Bandits—0
Neural Exploitation and Exploration of Contextual BanditsCode1
Reward Teaching for Federated Multi-armed Bandits—0
Stochastic Contextual Bandits with Graph-based Contexts—0
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits—0
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded RewardsCode0
Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement LearningCode0
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards—0
Optimal Activation of Halting Multi-Armed Bandit Models—0
A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed BanditsCode0
Learning Personalized Decision Support Policies—0
SmartChoices: Augmenting Software with Learned Implementations—0
BanditQ: Fair Bandits with Guaranteed Rewards—0
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion—0
Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms—0
Federated Learning for Heterogeneous Bandits with Unobserved Contexts—0
Adaptive Endpointing with Deep Contextual Multi-armed Bandits—0
An Empirical Evaluation of Federated Contextual Bandit Algorithms—0
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Benchmark Results

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