Export Reviews, Discussions, Author Feedback and Meta-Reviews

More specifically, it deals with learning the output kernel which defined over multiple tasks. Starting from the vector-valued RKHS formulation of the multi-task learning problem, the authors propose an output kernel learning algorithm that are able to learn both the multi-task learning function and task dependencies. The authors first provide an optimization formulation of the problem which is jointly convex. Then they show that this optimization can be solved without the positive-semidefiniteness constraint of the output kernel, which is computationally costly, and propose the use of a stochastic dual coordinate ascent method to solve it. Experiments on multi-task data sets and comparison in terms of performance and running time with previous multi-task learning algorithms are provided. The paper builds upon recent studies on multitask learning and output kernel learning.