Efficient Group Learning with Hypergraph Partition in Multi-task Learning

Abstract

Recently, wide concern has been aroused in multi-task learning (MTL) area, which assumes that affinitive tasks should own similar parameter representation so that joint learning is both appropriate and reciprocal. Researchers also find that imposing similar parameter representation constraint on dissimilar tasks may be harmful to MTL. However, it’s difficult to determine which tasks are similar. Z Kang et al [1] proposed to simultaneously learn the groups and parameters to address this problem. But the method is inefficient and cannot scale to large data. In this paper, using the property of the parameter matrix, we describe the group learning process as permuting the parameter matrix into a block diagonal matrix, which can be modeled as a hypergraph partition problem. The optimization algorithm scales well to large data. Extensive experiments demonstrate that our method is advantageous over existing MTL methods in terms of accuracy and efficiency.