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A Convex Formulation for Learning Task Relationships in Multi-Task Learning
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. Under this regularization framework, the objective
function of MTRL is convex. For efficiency, we use an alternating method to
learn the optimal model parameters for each task as well as the relationships
between tasks. We study MTRL in the symmetric multi-task learning setting and
then generalize it to the asymmetric setting as well. We also study the
relationships between MTRL and some existing multi-task learning methods.
Experiments conducted on a toy problem as well as several benchmark data sets
demonstrate the effectiveness of MTRL.Comment: Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010