Latest Articles for Regret

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.

Learn how hott items enhance online recommendation systems for better user experiences.

A new method to improve decision-making in changing systems using predictions.

This study addresses multi-period facility location challenges and provides effective solutions.

OBSI enhances decision-making by focusing on relevant features in online settings.

This study explores improving decision-making through risk-averse learning techniques.

Learn about decision-making strategies that minimize regret in experiments.

A new method improves performance by sharing insights between similar tasks.

Examining how rested bandits improve decision-making with breaks.

Introducing an innovative algorithm for model selection in reinforcement learning.

Learn how candy machines illustrate decision-making challenges and solutions in uncertain scenarios.

Learn about bandit problems and decision-making in uncertain environments.

Learn how to make quick and smart resource decisions efficiently.

Learn how asynchronous techniques improve real-time decision-making for AI agents.

Learn how to make better decisions amid uncertainty and minimize regret.

Learn effective strategies to outsmart unknown opponents in strategic games.

Dive into the world of harmonic games and their impact on decision-making.

Agents seek cooperation in changing environments to find optimal solutions.