A Convex Formulation for Learning Task Relationships in Multi-Task Learning

Understand

Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks.

• In this paper, we propose a regularization formulation for learning the relationships between tasks in multi-task learning.
• This formulation can be viewed as a novel generalization of the regularization framework for single-task learning.
• Besides modeling positive task correlation, our method, called multi-task relationship learning (MTRL), can also describe negative task correlation and identify outlier tasks based on the same underlying principle.