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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 501–525 of 1262 papers

TitleStatusHype
Variational Inference for Model-Free and Model-Based Reinforcement Learning—0
Exposure-Aware Recommendation using Contextual Bandits—0
Dynamic Global Sensitivity for Differentially Private Contextual Bandits—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
Understanding the stochastic dynamics of sequential decision-making processes: A path-integral analysis of multi-armed bandits—0
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits—0
Nonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial MarketCode0
Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits—0
Towards Soft Fairness in Restless Multi-Armed Bandits—0
SPRT-based Efficient Best Arm Identification in Stochastic Bandits—0
Online Learning with Off-Policy Feedback—0
Parallel Best Arm Identification in Heterogeneous Environments—0
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action SpacesCode0
Contextual Bandits with Large Action Spaces: Made PracticalCode0
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence ModelingCode1
Online SuBmodular + SuPermodular (BP) Maximization with Bandit FeedbackCode0
Model Selection in Reinforcement Learning with General Function Approximations—0
Instance-optimal PAC Algorithms for Contextual Bandits—0
Autonomous Drug Design with Multi-Armed Bandits—0
Ranking In Generalized Linear BanditsCode0
Two-Stage Neural Contextual Bandits for Personalised News RecommendationCode0
Joint Representation Training in Sequential Tasks with Shared Structure—0
Langevin Monte Carlo for Contextual BanditsCode1
Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms—0
On Private Online Convex Optimization: Optimal Algorithms in _p-Geometry and High Dimensional Contextual BanditsCode0
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Benchmark Results

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