Task Clustering

Task clustering, a subfield of machine learning, aims to group similar tasks together to improve the efficiency and performance of multi-task learning systems. Current research focuses on developing algorithms that effectively cluster tasks based on various criteria, including feature similarity, target relationships, and underlying task structures, often employing techniques like hierarchical clustering, contrastive learning, and set cover problem formulations. These advancements are significant because efficient task clustering enhances the generalization ability of models, improves sample efficiency in meta-reinforcement learning, and enables more robust and interpretable multi-task learning systems across diverse applications, such as mobile robotics and text classification.