Efficient Multioutput Gaussian Processes through Variational Inducing Kernels

Mauricio A. Álvarez; David Luengo; Michalis K. Titsias; Neil D. Lawrence

edit

Back to publications

Efficient Multioutput Gaussian Processes through Variational Inducing Kernels

Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence

Proceedings of the Thirteenth International Workshop on Artificial Intelligence and Statistics, PMLR 9:25-32, 2010.

Abstract

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 of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Álvarez and Lawrence @Alvarez:convolved08 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 @Titsias:variational09 to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.

Links

Cite this Paper

BibTeX


@InProceedings{Alvarez:efficient10,
  title = 	 {Efficient Multioutput {G}aussian Processes through Variational Inducing Kernels},
  author = 	 {Álvarez, Mauricio A. and Luengo, David and Titsias, Michalis K. and Lawrence, Neil D.},
  booktitle = 	 {Proceedings of the Thirteenth International Workshop on Artificial Intelligence and Statistics},
  pages = 	 {25--32},
  year = 	 {2010},
  editor = 	 {Teh, Yee Whye and Titterington, D. Michael},
  volume = 	 {9},
  address = 	 {Chia Laguna Resort, Sardinia, Italy},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v9/alvarez10a/alvarez10a.pdf},
  url = 	 {/publications/alvarez-efficient10.html},
  abstract = 	 {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 of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Álvarez and Lawrence @Alvarez:convolved08 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 @Titsias:variational09 to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.}
}

Endnote

%0 Conference Paper
%T Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
%A Mauricio A. Álvarez
%A David Luengo
%A Michalis K. Titsias
%A Neil D. Lawrence
%B Proceedings of the Thirteenth International Workshop on Artificial Intelligence and Statistics
%D 2010
%E Yee Whye Teh
%E D. Michael Titterington	
%F Alvarez:efficient10
%I PMLR
%P 25--32
%U /publications/alvarez-efficient10.html
%V 9
%X 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 of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Álvarez and Lawrence @Alvarez:convolved08 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 @Titsias:variational09 to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.

RIS


TY  - CPAPER
TI  - Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
AU  - Mauricio A. Álvarez
AU  - David Luengo
AU  - Michalis K. Titsias
AU  - Neil D. Lawrence
BT  - Proceedings of the Thirteenth International Workshop on Artificial Intelligence and Statistics
DA  - 2010/03/31
ED  - Yee Whye Teh
ED  - D. Michael Titterington	
ID  - Alvarez:efficient10
PB  - PMLR
VL  - 9
SP  - 25
EP  - 32
L1  - http://proceedings.mlr.press/v9/alvarez10a/alvarez10a.pdf
UR  - /publications/alvarez-efficient10.html
AB  - 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 of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Álvarez and Lawrence @Alvarez:convolved08 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 @Titsias:variational09 to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.
ER  -

APA


Álvarez, M.A., Luengo, D., Titsias, M.K. & Lawrence, N.D.. (2010). Efficient Multioutput Gaussian Processes through Variational Inducing Kernels. Proceedings of the Thirteenth International Workshop on Artificial Intelligence and Statistics 9:25-32 Available from /publications/alvarez-efficient10.html.