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Learning Gaussian Processes from Multiple Tasks

Kai Yu, Volker Tresp, and Anton Schwaighofer


We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equivalence between parametric linear models and nonparametric Gaussian processes (GPs). The resulting models can be learned easily via an EM-algorithm. Empirical studies on multi-label text categorization suggest that the presented models allow accurate solutions of these multi-task problems.


Publication typeInproceedings
Published inMachine Learning: Proceedings of the 22nd International Conference (ICML 2005)
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