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Multi-task learning with task relations

: Xu, Z.; Kersting, K.


Cook, D.:
IEEE 11th International Conference on Data Mining, ICDM 2011
Piscataway: IEEE, 2011
ISBN: 978-1-457-72075-8
ISBN: 978-0-7695-4408-3
International Conference on Data Mining (ICDM) <11, 2011, Vancouver>
Fraunhofer IAIS ()

Multi-task and relational learning with Gaussian processes are two active but also orthogonal areas of research. So far, there has been few attempt at exploring relational information within multi-task Gaussian processes. While existing relational Gaussian process methods have focused on relations among entities and in turn could be employed within an individual task, we develop a class of Gaussian process models which incorporates relational information across multiple tasks. As we will show, inference and learning within the resulting class of models, called relational multi-task Gaussian processes, can be realized via a variational EM algorithm. Experimental results on synthetic and real-world datasets verify the usefulness of this approach: The observed relational knowledge at the level of tasks can indeed reveal additional pairwise correlations between tasks of interest and, in turn, improve prediction performance.