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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 501–550 of 1262 papers

TitleStatusHype
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees—0
PAC-Bayesian Offline Contextual Bandits With Guarantees—0
Conditionally Risk-Averse Contextual BanditsCode0
Fast Beam Alignment via Pure Exploration in Multi-armed BanditsCode0
Optimal Contextual Bandits with Knapsacks under Realizability via Regression OraclesCode0
Vertical Federated Linear Contextual Bandits—0
Contextual bandits with concave rewards, and an application to fair ranking—0
Simulated Contextual Bandits for Personalization Tasks from Recommendation DatasetsCode0
Maximum entropy exploration in contextual bandits with neural networks and energy based models—0
Constant regret for sequence prediction with limited advice—0
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs—0
ProtoBandit: Efficient Prototype Selection via Multi-Armed Bandits—0
Replicable Bandits—0
On Best-Arm Identification with a Fixed Budget in Non-Parametric Multi-Armed Bandits—0
Off-Policy Risk Assessment in Markov Decision Processes—0
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits—0
Towards Robust Off-Policy Evaluation via Human Inputs—0
Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems—0
Risk-aware linear bandits with convex loss—0
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits—0
Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health—0
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits—0
Multi-Armed Bandits with Self-Information Rewards—0
Exposure-Aware Recommendation using Contextual Bandits—0
Variational Inference for Model-Free and Model-Based Reinforcement Learning—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
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
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
Combinatorial Pure Exploration of Causal Bandits—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
Distributed Differential Privacy in Multi-Armed Bandits—0
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits—0
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Benchmark Results

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