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Collaborating differently on different topics: a multi-relational approach to multi-task learning

Gupta, Sunil Kumar, Rana, Santu, Phung, Dinh and Venkatesh, Svetha 2015, Collaborating differently on different topics: a multi-relational approach to multi-task learning. In Cao, Tru, Lim, Ee-Peng, Zhou, Zhi-Hua, Ho, Tu-Bao, Cheung, David and Motoda, Hiroshi (ed), Advances in knowledge discovery and data mining 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I, Springer, Berlin, Germany, pp.303-316, doi: 10.1007/978-3-319-18038-0_24.

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Title Collaborating differently on different topics: a multi-relational approach to multi-task learning
Author(s) Gupta, Sunil KumarORCID iD for Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, Svetha
Title of book Advances in knowledge discovery and data mining 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I
Editor(s) Cao, Tru
Lim, Ee-Peng
Zhou, Zhi-Hua
Ho, Tu-Bao
Cheung, David
Motoda, Hiroshi
Publication date 2015
Series Lecture notes in computer science; v.9077
Chapter number 24
Total chapters 58
Start page 303
End page 316
Total pages 14
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
Summary Multi-task learning offers a way to benefit from synergy of multiple related prediction tasks via their joint modeling. Current multi-task techniques model related tasks jointly, assuming that the tasks share the same relationship across features uniformly. This assumption is seldom true as tasks may be related across some features but not others. Addressing this problem, we propose a new multi-task learning model that learns separate task relationships along different features. This added flexibility allows our model to have a finer and differential level of control in joint modeling of tasks along different features. We formulate the model as an optimization problem and provide an efficient, iterative solution. We illustrate the behavior of the proposed model using a synthetic dataset where we induce varied feature-dependent task relationships: positive relationship, negative relationship, no relationship. Using four real datasets, we evaluate the effectiveness of the proposed model for many multi-task regression and classification problems, and demonstrate its superiority over other state-of-the-art multi-task learning models
ISBN 9783319180373
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-18038-0_24
Field of Research 080109 Pattern Recognition and Data Mining
08 Information And Computing Sciences
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076880

Document type: Book Chapter
Collection: Centre for Physical Activity and Nutrition Research
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