AISTATS 2015poster51 citations
Convex Multi-Task Learning by Clustering
Abstract
We consider the problem of multi-task learning in which tasks belong to hidden clusters. We formulate the learning problem as a novel convex optimization problem in which linear classifiers are combinations of (a small number of) some basis. Our formulation jointly learns both the basis and the linear combination. We propose a scalable optimization algorithm for finding the optimal solution. Our new methods outperform existing state-of-the-art methods on multi-task sentiment classification tasks.
BibTeX
@InProceedings{pmlr-v38-barzilai15,
title = {{Convex Multi-Task Learning by Clustering}},
author = {Barzilai, Aviad and Crammer, Koby},
booktitle = {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics},
pages = {65--73},
year = {2015},
editor = {Lebanon, Guy and Vishwanathan, S. V. N.},
volume = {38},
series = {Proceedings of Machine Learning Research},
address = {San Diego, California, USA},
month = {09--12 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v38/barzilai15.pdf},
url = {https://proceedings.mlr.press/v38/barzilai15.html},
abstract = {We consider the problem of multi-task learning in which tasks belong to hidden clusters. We formulate the learning problem as a novel convex optimization problem in which linear classifiers are combinations of (a small number of) some basis. Our formulation jointly learns both the basis and the linear combination. We propose a scalable optimization algorithm for finding the optimal solution. Our new methods outperform existing state-of-the-art methods on multi-task sentiment classification tasks.}
}