arXiv:2510.26284v2 Announce Type: replace 
Abstract: Multi-task learning in contextual bandits has attracted significant research interest due to its potential to enhance decision-making across multiple related tasks by leveraging shared structures and task-specific heterogeneity. In this article, we propose a novel hierarchical Bayesian framework for learning in various bandit instances. This framework captures both the heterogeneity and the correlations among different bandit instances through a hierarchical Bayesian model, enabling effective information sharing while accommodating instance-specific variations. Unlike previous methods that overlook the learning of the covariance structure across bandits, we introduce an empirical Bayesian approach to estimate the covariance matrix of the prior distribution. This enhances both the practicality and flexibility of learning across multi-bandits. Building on this approach, we develop two efficient algorithms: ebmTS (Empirical Bayesian Multi-Bandit Thompson Sampling) and ebmUCB (Empirical Bayesian Multi-Bandit Upper Confidence Bound), both of which incorporate the estimated prior into the decision-making process. We provide the frequentist regret upper bounds for the proposed algorithms, thereby filling a research gap in the field of multi-bandit problems. Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of our algorithms, particularly in complex environments. Our methods achieve lower cumulative regret compared to existing techniques, highlighting their effectiveness in balancing exploration and exploitation across multi-bandits.

تقدم دراسة جديدة إطارًا بايزيًا هرميًا للتعلم متعدد المهام في سياقات الباندت، مما قد يحسن بشكل كبير من اتخاذ القرارات عبر المهام ذات الصلة. لا تعالج هذه الطريقة التحديات الفريدة لكل مهمة فحسب، بل تستفيد أيضًا من الروابط بينها، مما يجعلها تقدمًا واعدًا في مجال التعلم الآلي. مع استمرار الباحثين في استكشاف هذه الطريقة المبتكرة، فإن لديها القدرة على تحسين تطبيقات متنوعة تعتمد على اتخاذ قرارات فعالة.

Un nuevo estudio presenta un marco bayesiano jerárquico para el aprendizaje multitarea en bandits contextuales, lo que podría mejorar significativamente la toma de decisiones en tareas relacionadas. Este enfoque no solo aborda los desafíos únicos de cada tarea, sino que también aprovecha las conexiones entre ellas, lo que lo convierte en un avance prometedor en el campo del aprendizaje automático. A medida que los investigadores continúan explorando este método innovador, tiene el potencial de mejorar diversas aplicaciones que dependen de una toma de decisiones efectiva.

Une nouvelle étude présente un cadre bayésien hiérarchique pour l'apprentissage multitâche dans les bandits contextuels, ce qui pourrait améliorer considérablement la prise de décision dans des tâches connexes. Cette approche aborde non seulement les défis uniques de chaque tâche, mais tire également parti des connexions entre elles, ce qui en fait une avancée prometteuse dans le domaine de l'apprentissage automatique. Alors que les chercheurs continuent d'explorer cette méthode innovante, elle a le potentiel d'améliorer diverses applications reposant sur une prise de décision efficace.

A new study introduces a hierarchical Bayesian framework for multi-task learning in contextual bandits, which could significantly improve decision-making across related tasks. This approach not only addresses the unique challenges of each task but also leverages the connections between them, making it a promising advancement in the field of machine learning. As researchers continue to explore this innovative method, it holds the potential to enhance various applications that rely on effective decision-making.

Empirical Bayesian Multi-Bandit Learning

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Empirical Bayesian Multi-Bandit Learning

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