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Multi-task Learning with Task Relations

Zhao Xu; Kristian Kersting
In: Diane J. Cook; Jian Pei; Wei Wang; Osmar R. Zaïane; Xindong Wu (Hrsg.). 11th IEEE International Conference on Data Mining. IEEE International Conference on Data Mining (ICDM-2011), December 11-14, Vancouver, BC, Canada, Pages 884-893, IEEE Computer Society, 2011.


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 pair wise correlations between tasks of interest and, in turn, improve prediction performance.

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