  We consider multi-task learning, which simultaneously learns related
prediction tasks, to improve generalization performance. We factorize a
coefficient matrix as the product of two matrices based on a low-rank
assumption. These matrices have sparsities to simultaneously perform variable
selection and learn and overlapping group structure among the tasks. The
resulting bi-convex objective function is minimized by alternating optimization
where sub-problems are solved using alternating direction method of multipliers
and accelerated proximal gradient descent. Moreover, we provide the performance
bound of the proposed method. The effectiveness of the proposed method is
validated for both synthetic and real-world datasets.
