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Factorial multi-task learning : a Bayesian nonparametric approach

Version 2 2024-06-03, 17:12
Version 1 2014-11-24, 11:40
conference contribution
posted on 2024-06-03, 17:12 authored by Sunil GuptaSunil Gupta, QD Phung, Svetha VenkateshSvetha Venkatesh
Multi-task learning is a paradigm shown to improve the performance of related tasks through their joint learning. However, for real-world data, it is usually difficult to assess the task relatedness and joint learning with unrelated tasks may lead to serious performance degradations. To this end, we propose a framework that groups the tasks based on their relatedness in a subspace and allows a varying degree of relatedness among tasks by sharing the subspace bases across the groups. This provides the flexibility of no sharing when two sets of tasks are unrelated and partial/total sharing when the tasks are related. Importantly, the number of task-groups and the subspace dimensionality are automatically inferred from the data. To realize our framework, we introduce a novel Bayesian nonparametric prior that extends the traditional hierarchical beta process prior using a Dirichlet process to permit potentially infinite number of child beta processes. We apply our model for multi-task regression and classification applications. Experimental results using several synthetic and real datasets show the superiority of our model to other recent multi-task learning methods. Copyright 2013 by the author(s).

History

Pagination

1694-1702

Location

Atlanta, Ga.

Start date

2013-06-16

End date

2013-06-21

Language

eng

Publication classification

E Conference publication, E1.1 Full written paper - refereed

Copyright notice

2013, IMLS

Editor/Contributor(s)

Dasgupta S, McAllester D

Title of proceedings

ICML 2013 : Proceedings of the Machine Learning 2013 International Conference

Event

Machine Learning. International Conference (30th : 2013 : Atlanta, Ga.)

Publisher

International Machine Learning Society (IMLS)

Place of publication

[Atlanta, Ga.]

Series

JMLR Workshop and Conference Proceedings Vol. 28