Multi-task Sparse Structure Learning
Published in CIKM'14, 2014
Authors: AR Goncalves, P Das, S Chatterjee, V Sivakumar, FJ Von Zuben, A Banerjee
In this paper, we present a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of task relationships. In particular, we consider a joint estimation problem of the task relationship structure and the individual task parameters, which is solved using alternating minimization. The task relationship structure learning component builds on recent advances in structure learning of Gaussian graphical models based on sparse estimators of the precision (inverse covariance) matrix.