Regularization for Multi-Output Learning
Lorenzo Rosasco


In many practical problems, it is convenient to model the object of interest as a function with multiple outputs. In machine learning, this problem typically goes under the name of multi-task or multi-output learning. We present some concepts and algorithms to solve these kinds of problems using kernel methods and regularization.


Slides for this lecture: PDF.

Suggested Reading

  • T. Evgeniou and C.A. Micchelli and M. Pontil. "Learning multiple tasks with kernel methods", J. Machine Learning Research, 6: 615--637, 2005.
  • M. Pontil and C.A. Micchelli. "Kernels for multi-task learning", NIPS 2004.
  • M. Pontil and C.A. Micchelli, "On learning vector-valued functions", Neural Computation, 17: 177--204, 2005.
  • Multi-Output Learning with Spectral Regularization Methods (in preparation) Baldassarre, L., Barla, A. Rosasco, L. and Verri, A.