The Gaussian Process Autoregressive Regression Model (GPAR)

James Requeima, William Tebbutt, Wessel Bruinsma, Richard E. Turner
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:1860-1869, 2019.

Abstract

Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically yields models that are computationally demanding and have limited representational power. We present the Gaussian Process Autoregressive Regression (GPAR) model, a scalable multi-output GP model that is able to capture nonlinear, possibly input-varying, dependencies between outputs in a simple and tractable way: the product rule is used to decompose the joint distribution over the outputs into a set of conditionals, each of which is modelled by a standard GP. GPAR’s efficacy is demonstrated on a variety of synthetic and real-world problems, outperforming existing GP models and achieving state-of-the-art performance on established benchmarks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-requeima19a, title = {The Gaussian Process Autoregressive Regression Model (GPAR)}, author = {Requeima, James and Tebbutt, William and Bruinsma, Wessel and Turner, Richard E.}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {1860--1869}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/requeima19a/requeima19a.pdf}, url = {https://proceedings.mlr.press/v89/requeima19a.html}, abstract = {Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically yields models that are computationally demanding and have limited representational power. We present the Gaussian Process Autoregressive Regression (GPAR) model, a scalable multi-output GP model that is able to capture nonlinear, possibly input-varying, dependencies between outputs in a simple and tractable way: the product rule is used to decompose the joint distribution over the outputs into a set of conditionals, each of which is modelled by a standard GP. GPAR’s efficacy is demonstrated on a variety of synthetic and real-world problems, outperforming existing GP models and achieving state-of-the-art performance on established benchmarks.} }
Endnote
%0 Conference Paper %T The Gaussian Process Autoregressive Regression Model (GPAR) %A James Requeima %A William Tebbutt %A Wessel Bruinsma %A Richard E. Turner %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-requeima19a %I PMLR %P 1860--1869 %U https://proceedings.mlr.press/v89/requeima19a.html %V 89 %X Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically yields models that are computationally demanding and have limited representational power. We present the Gaussian Process Autoregressive Regression (GPAR) model, a scalable multi-output GP model that is able to capture nonlinear, possibly input-varying, dependencies between outputs in a simple and tractable way: the product rule is used to decompose the joint distribution over the outputs into a set of conditionals, each of which is modelled by a standard GP. GPAR’s efficacy is demonstrated on a variety of synthetic and real-world problems, outperforming existing GP models and achieving state-of-the-art performance on established benchmarks.
APA
Requeima, J., Tebbutt, W., Bruinsma, W. & Turner, R.E.. (2019). The Gaussian Process Autoregressive Regression Model (GPAR). Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:1860-1869 Available from https://proceedings.mlr.press/v89/requeima19a.html.

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