Collaborative Multi-output Gaussian Processes

Trung Nguyen, Edwin Bonilla National ICT Australia
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:66-75, 2014.

Abstract

We introduce the collaborative multi-output Gaussian process (GP) model for learning dependent tasks with very large datasets. The model fosters task correlations by mixing sparse processes and sharing multiple sets of inducing points. This facilitates the application of variational inference and the derivation of an evidence lower bound that decomposes across inputs and outputs. We learn all the parameters of the model in a single stochastic optimization framework that scales to a large number of observations per output and a large number of outputs. We demonstrate our approach on a toy problem, two medium-sized datasets and a large dataset. The model achieves superior performance compared to single output learning and previous multi-output GP models, confirming the benefits of correlating sparsity structure of the outputs via the inducing points.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-nguyen14a, title = {Collaborative Multi-output {G}aussian Processes}, author = {Nguyen, Trung and Australia, Edwin Bonilla National ICT}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {66--75}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/nguyen14a/nguyen14a.pdf}, url = {https://proceedings.mlr.press/r12/nguyen14a.html}, abstract = {We introduce the collaborative multi-output Gaussian process (GP) model for learning dependent tasks with very large datasets. The model fosters task correlations by mixing sparse processes and sharing multiple sets of inducing points. This facilitates the application of variational inference and the derivation of an evidence lower bound that decomposes across inputs and outputs. We learn all the parameters of the model in a single stochastic optimization framework that scales to a large number of observations per output and a large number of outputs. We demonstrate our approach on a toy problem, two medium-sized datasets and a large dataset. The model achieves superior performance compared to single output learning and previous multi-output GP models, confirming the benefits of correlating sparsity structure of the outputs via the inducing points.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Collaborative Multi-output Gaussian Processes %A Trung Nguyen %A Edwin Bonilla National ICT Australia %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-nguyen14a %I PMLR %P 66--75 %U https://proceedings.mlr.press/r12/nguyen14a.html %V R12 %X We introduce the collaborative multi-output Gaussian process (GP) model for learning dependent tasks with very large datasets. The model fosters task correlations by mixing sparse processes and sharing multiple sets of inducing points. This facilitates the application of variational inference and the derivation of an evidence lower bound that decomposes across inputs and outputs. We learn all the parameters of the model in a single stochastic optimization framework that scales to a large number of observations per output and a large number of outputs. We demonstrate our approach on a toy problem, two medium-sized datasets and a large dataset. The model achieves superior performance compared to single output learning and previous multi-output GP models, confirming the benefits of correlating sparsity structure of the outputs via the inducing points. %Z Reissued by PMLR on 04 October 2026.
APA
Nguyen, T. & Australia, E.B.N.I.. (2014). Collaborative Multi-output Gaussian Processes. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:66-75 Available from https://proceedings.mlr.press/r12/nguyen14a.html. Reissued by PMLR on 04 October 2026.

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