CAREER: A Principled Framework for Multi-Task Representation Learning for Scalable, Decentralized, and Safe Sequential Decision-Making

Real-world systems are composed of interconnected entities that collaborate to perform diverse yet interrelated tasks, often requiring sequential decision-making. Over the past decade, multi-task learning has emerged as a powerful paradigm for collaborative learning, significantly enhancing efficiency while enabling privacy-preserving knowledge…