Latest Articles for Regret

New approaches improve resource allocation in online operations, balancing learning and decision-making.

Examining the emotional struggles of mothers facing regret in Japan.

This paper discusses algorithms to improve decision-making in contextual bandit scenarios.

Examining exploration and adaptability in reinforcement learning algorithms.

A guide to making decisions under uncertainty with multi-armed bandit techniques.

This study examines how prior knowledge improves decision-making in reinforcement learning.

A new algorithm improves online learning by adapting to incoming data effectively.

An overview of decision-making in combinatorial bandit problems.

A new approach to caching offers better performance in dynamic environments.

Explore how drift analysis improves the efficiency of AI algorithms.

A look into online learning algorithms and expert interactions.

A new approach combines privacy protections with contextual bandit learning.

Strategies for optimizing systems by minimizing regret in uncertain environments.

This article discusses how transformers can improve decision-making tasks in various areas.

Examine how information and regret shape our choices.

A new method that speeds up decisions in the matroid semi-bandit problem.

Innovative strategies for maximizing rewards in decision-making environments.

A new algorithm improves learning in constrained environments using posterior sampling.

A new model for better decision-making using historical data.

A look at improving threshold policies using experimental data and new methods.

Exploring limited feedback in machine learning classification tasks.

This study examines how future predictions enhance online classification performance.

Examining how algorithms can protect privacy while optimizing decisions in personalized medicine.

This study explores the impact of predictions on individual actions and outcomes.

CMDPs enhance decision-making by considering context in various applications.

A deep dive into U-calibration error and its role in predicting outcomes.

Learn how to navigate interference in data-driven decisions.

A look at the FedQ-Advantage algorithm in federated reinforcement learning.

An overview of decision-making under uncertainty with the streaming model.

New algorithm achieves low regret in dynamic learning environments without prior knowledge.

A fresh method for managing population changes in real-time.

A look into minimizing regret amid changing algorithms in online learning.

A new algorithm offers effective solutions for average-reward decision-making tasks.

This article explores the challenges of achieving fairness in machine learning across different demographic groups.

Exploring new algorithms for multiclass classification under limited feedback conditions.

New algorithms improve quantum state learning with minimal disturbance and lower regret.

Discover how best-action queries improve online learning outcomes and reduce decision-making losses.

Explore the challenges of adapting to changing rewards in decision-making.

Using neural networks to enhance decision-making in preference-based contexts.

A look at Binary Search Trees and Huffman trees under uncertainty.