Factorial multi-task learning : a Bayesian nonparametric approach Gupta, Sunil Kumar, Phung, Dinh and Venkatesh, Svetha 2013, Factorial multi-task learning : a Bayesian nonparametric approach, in ICML 2013 : Proceedings of the Machine Learning 2013 International Conference, International Machine Learning Society (IMLS), [Atlanta, Ga.], pp. 1694-1702. Attached Files Name Description MIMEType Size Downloads Title Factorial multi-task learning : a Bayesian nonparametric approach Author(s) Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Machine Learning. International Conference (30th : 2013 : Atlanta, Ga.) Conference location Atlanta, Ga. Conference dates 16 - 21 Jun. 2013 Title of proceedings ICML 2013 : Proceedings of the Machine Learning 2013 International Conference Editor(s) Dasgupta, Sanjoy McAllester, David Publication date 2013 Series JMLR Workshop and Conference Proceedings Vol. 28 Conference series Machine Learning International Conference Start page 1694 End page 1702 Total pages 9 Publisher International Machine Learning Society (IMLS) Place of publication [Atlanta, Ga.] Keyword(s) artificial intelligence software engineering dirichlet process infinite numbers joint learning multitask learning non-parametric nonparametric approaches performance degradation task relatedness Summary 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). Language eng Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1.1 Full written paper - refereed HERDC collection year 2013 Copyright notice ©2013, IMLS Persistent URL http://hdl.handle.net/10536/DRO/DU:30067638 Document type: Conference Paper Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 24 Nov 2014, 10:41:12 EST Mon, 24 Nov 2014, 11:04:07 EST Mon, 24 Nov 2014, 12:00:19 EST Fri, 28 Nov 2014, 12:34:07 EST Fri, 28 Nov 2014, 12:48:41 EST Tue, 02 Dec 2014, 12:47:07 EST Wed, 17 Dec 2014, 08:23:19 EST Tue, 10 Feb 2015, 07:36:40 EST Thu, 14 Apr 2016, 09:59:07 EST Thu, 14 Apr 2016, 10:00:36 EST Thu, 28 Apr 2016, 11:35:00 EST Wed, 07 Sep 2016, 07:58:39 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 9 times in Scopus Search Google Scholar Access Statistics: 239 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Mon, 24 Nov 2014, 10:41:12 EST

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