Efficient Multioutput Gaussian Processes through Variational Inducing Kernels


Mauricio Álvarez, David Luengo, Michalis Titsias, Neil D. Lawrence ;
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:25-32, 2010.


Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way to construct such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Alvarez and Lawrence recently presented a sparse approximation for CPs that enabled efficient inference. In this paper, we extend this work in two directions: we introduce the concept of variational inducing functions to handle potential non-smooth functions involved in the kernel CP construction and we consider an alternative approach to approximate inference based on variational methods, extending the work by Titsias (2009) to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.

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