@conference {58,
	title = {Multi-Task Learning with Group-Specific Feature Space Sharing},
	booktitle = {Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2015},
	series = {Lecture Notes in Computer Science},
	volume = {9285},
	year = {2015},
	note = {Acceptance rate 23.4\% (89/380).},
	pages = {120-136},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {When faced with learning a set of inter-related tasks from a limited amount of usable data, learning each task independently may lead to poor generalization performance. (MTL) exploits the latent relations between tasks and overcomes data scarcity limitations by co-learning all these tasks simultaneously to offer improved performance. We propose a novel Multi-Task Multiple Kernel Learning framework based on Support Vector Machines for binary classification tasks. By considering pair-wise task affinity in terms of similarity between a pair{\textquoteright}s respective feature spaces, the new framework, compared to other similar MTL approaches, offers a high degree of flexibility in determining how similar feature spaces should be, as well as which pairs of tasks should share a common feature space in order to benefit overall performance. The associated optimization problem is solved via a block coordinate descent, which employs a consensus-form Alternating Direction Method of Multipliers algorithm to optimize the Multiple Kernel Learning weights and, hence, to determine task affinities. Empirical evaluation on seven data sets exhibits a statistically significant improvement of our framework{\textquoteright}s results compared to the ones of several other Clustered Multi-Task Learning methods.},
	doi = {10.1007/978-3-319-23525-7_8},
	author = {Niloofar Yousefi and Michael Georgiopoulos and Georgios C. Anagnostopoulos}
}
