Latest Articles for Regret

Learn how multi-arm bandit algorithms enhance item selection in recommendation systems.

A new framework combines expert opinions with multi-armed bandit strategies for better decisions.

New methods improve predictions of ice sheet behavior and sea level rise.

Introducing Variational Inference Thompson Sampling for efficient decision-making.

This article discusses resilience in LLM-generated automation systems.

A new algorithm improves efficiency in online learning and decision-making.

This study examines how to improve treatment decisions using kernel regression methods.

A method to balance rewards and resources using clustered contextual bandits.

This article discusses methods for protecting user data in recommendation systems.

Examining learning methods for effective gameplay in uncertain environments.

An analysis of Thompson Sampling and its variant for improved decision-making.

Exploring DiSC-UCB algorithm for effective action selection in uncertain environments.

Exploring the emotions of guilt and regret in decision-making.

This study presents methods to reduce regret in billboard advertising slot allocation.

Learn how to minimize regret in billboard ad spaces.

New algorithms tackle challenges in continuous state-action spaces of Reinforcement Learning.

A look into how we assess the quality of forecasts.

Learn how to manage unknown systems effectively with agnostic control strategies.

Learn strategies for controlling systems despite unknown variables.

Learn how SMPC balances safety and performance in uncertain systems.

Addressing the challenge of privacy in data-driven decision-making for healthcare.

Investigating contracts and Agent performance in repeated hiring situations.

Algorithms enhance player strategies in uncertain game environments.

A look into the importance and challenges of online quantile regression.

This study enhances decision-making in limited exploration scenarios using prior information.

This article presents a method for clients with diverse objectives in federated bandit learning.

A look into challenges of making predictions in complex dynamical systems.

New algorithms tackle challenges in adversarial MDPs without needing prior loss knowledge.

A new method enhances how robots predict human behavior and improve safety.

A new approach to linear bandits addresses feedback noise for better decision-making.

A look at local correlated equilibria and their impact on player strategies.

Learn effective methods for addressing the sleeping bandit problem in decision-making.

Examining Stackelberg games and pricing strategies in supply chains.

A look at equitable resource sharing strategies in gaming and real-life scenarios.

This article examines online learning systems and their challenges regarding unknown constraints.

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.