Multi-task learning: Difference between revisions

Revision as of 15:19, 13 January 2021

Revision as of 15:23, 30 April 2021

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==Software package==

The Multi-Task Learning via StructurAl Regularization (MALSAR) Matlab package<ref>Zhou, J., Chen, J. and Ye, J. MALSAR: Multi-tAsk Learning via StructurAl Regularization. Arizona State University, 2012. http://www.public.asu.edu/~jye02/Software/MALSAR. [http://www.public.asu.edu/~jye02/Software/MALSAR/Manual.pdf On-line manual]</ref> implements the following multi-task learning algorithms:

* Mean-Regularized Multi-Task Learning<ref>Evgeniou, T., & Pontil, M. (2004). [https://web.archive.org/web/20171212193041/https://pdfs.semanticscholar.org/1ea1/91c70559d21be93a4d128f95943e80e1b4ff.pdf Regularized multi–task learning]. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 109–117).</ref><ref>{{cite journal | last1 = Evgeniou | first1 = T. | last2 = Micchelli | first2 = C. | last3 = Pontil | first3 = M. | year = 2005 | title = Learning multiple tasks with kernel methods | url = http://jmlr.org/papers/volume6/evgeniou05a/evgeniou05a.pdf | journal = Journal of Machine Learning Research | volume = 6 | page = 615 }}</ref>

* Multi-Task Learning with Joint Feature Selection<ref>{{cite journal | last1 = Argyriou | first1 = A. | last2 = Evgeniou | first2 = T. | last3 = Pontil | first3 = M. | year = 2008a | title = Convex multi-task feature learning | journal = Machine Learning | volume = 73 | issue = 3| pages = 243–272 | doi=10.1007/s10994-007-5040-8| doi-access = free }}</ref>

* Robust Multi-Task Feature Learning<ref>Chen, J., Zhou, J., & Ye, J. (2011). [https://www.academia.edu/download/44101186/Integrating_low-rank_and_group-sparse_st20160325-15067-1mftmbg.pdf Integrating low-rank and group-sparse structures for robust multi-task learning]. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining.</ref>