Sparse Convolved Gaussian Processes for Multi-output Regression

Mauricio A. Álvarez; Neil D. Lawrence

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Sparse Convolved Gaussian Processes for Multi-output Regression

Mauricio A. Álvarez, Neil D. Lawrence

Advances in Neural Information Processing Systems, MIT Press 21:57-64, 2008.

Abstract

We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to establish dependencies between output variables, where each latent function is represented as a GP. Based on these latent functions, we establish an approximation scheme using a conditional independence assumption between the output processes, leading to an approximation of the full covariance which is determined by the locations at which the latent functions are evaluated. We show results of the proposed methodology for synthetic data and real world applications on pollution prediction and a sensor network.

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Cite this Paper

BibTeX


@InProceedings{Alvarez:convolved08,
  title = 	 {Sparse Convolved {G}aussian Processes for Multi-output Regression},
  author = 	 {Álvarez, Mauricio A. and Lawrence, Neil D.},
  booktitle = 	 {Advances in Neural Information Processing Systems},
  pages = 	 {57--64},
  year = 	 {2008},
  editor = 	 {Koller, Daphne and Schuurmans, Dale and Bengio, Yoshua and Bottou, Leon},
  volume = 	 {21},
  address = 	 {Cambridge, MA},
  publisher =    {MIT Press},
  pdf = 	 {https://papers.nips.cc/paper/2008/file/149e9677a5989fd342ae44213df68868-Paper.pdf},
  url = 	 {/publications/alvarez-convolved08.html},
  abstract = 	 {We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to establish dependencies between output variables, where each latent function is represented as a GP. Based on these latent functions, we establish an approximation scheme using a conditional independence assumption between the output processes, leading to an approximation of the full covariance which is determined by the locations at which the latent functions are evaluated. We show results of the proposed methodology for synthetic data and real world applications on pollution prediction and a sensor network.}
}

Endnote

%0 Conference Paper
%T Sparse Convolved Gaussian Processes for Multi-output Regression
%A Mauricio A. Álvarez
%A Neil D. Lawrence
%B Advances in Neural Information Processing Systems
%D 2008
%E Daphne Koller
%E Dale Schuurmans
%E Yoshua Bengio
%E Leon Bottou	
%F Alvarez:convolved08
%I MIT Press
%P 57--64
%U /publications/alvarez-convolved08.html
%V 21
%X We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to establish dependencies between output variables, where each latent function is represented as a GP. Based on these latent functions, we establish an approximation scheme using a conditional independence assumption between the output processes, leading to an approximation of the full covariance which is determined by the locations at which the latent functions are evaluated. We show results of the proposed methodology for synthetic data and real world applications on pollution prediction and a sensor network.

RIS


TY  - CPAPER
TI  - Sparse Convolved Gaussian Processes for Multi-output Regression
AU  - Mauricio A. Álvarez
AU  - Neil D. Lawrence
BT  - Advances in Neural Information Processing Systems
DA  - 2008/12/08
ED  - Daphne Koller
ED  - Dale Schuurmans
ED  - Yoshua Bengio
ED  - Leon Bottou	
ID  - Alvarez:convolved08
PB  - MIT Press
VL  - 21
SP  - 57
EP  - 64
L1  - https://papers.nips.cc/paper/2008/file/149e9677a5989fd342ae44213df68868-Paper.pdf
UR  - /publications/alvarez-convolved08.html
AB  - We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to establish dependencies between output variables, where each latent function is represented as a GP. Based on these latent functions, we establish an approximation scheme using a conditional independence assumption between the output processes, leading to an approximation of the full covariance which is determined by the locations at which the latent functions are evaluated. We show results of the proposed methodology for synthetic data and real world applications on pollution prediction and a sensor network.
ER  -

APA


Álvarez, M.A. & Lawrence, N.D.. (2008). Sparse Convolved Gaussian Processes for Multi-output Regression. Advances in Neural Information Processing Systems 21:57-64 Available from /publications/alvarez-convolved08.html.