Imperial College London


Faculty of EngineeringDepartment of Electrical and Electronic Engineering




+44 (0)20 7594 6173c.ciliberto CV




1003Electrical EngineeringSouth Kensington Campus






BibTex format

author = {Ciliberto, C and Mroueh, Y and Poggio, T and Rosasco, L},
journal = {32nd International Conference on Machine Learning, ICML 2015},
pages = {1548--1557},
title = {Convex learning of multiple tasks and their structure},
volume = {2},
year = {2015}

RIS format (EndNote, RefMan)

AB - Copyright © 2015 by the author(s). Reducing the amount of human supervision is a key problem in machine learning and a natural approach is that of exploiting the relations (structure) among different tasks. This is the idea at the core of multi-task learning. In this context a fundamental question is how to incorporate the tasks structure in the learning problem. We tackle this question by studying a general computational framework that allows to encode a-priori knowledge of the tasks structure in the form of a convex penalty; in this setting a variety of previously proposed methods can be recovered as special cases, including linear and non-linear approaches. Within this framework, we show that tasks and their structure can be efficiently learned considering a convex optimization problem that can be approached by means of block coordinate methods such as alternating minimization and for which we prove convergence to the global minimum.
AU - Ciliberto,C
AU - Mroueh,Y
AU - Poggio,T
AU - Rosasco,L
EP - 1557
PY - 2015///
SP - 1548
TI - Convex learning of multiple tasks and their structure
T2 - 32nd International Conference on Machine Learning, ICML 2015
VL - 2
ER -