Gaussian processes with monotonicity information

Jaakko Riihimäki, Aki Vehtari
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:645-652, 2010.

Abstract

A method for using monotonicity information in multivariate Gaussian process regression and classification is proposed. Monotonicity information is introduced with virtual derivative observations, and the resulting posterior is approximated with expectation propagation. Behaviour of the method is illustrated with artificial regression examples, and the method is used in a real world health care classification problem to include monotonicity information with respect to one of the covariates.

Cite this Paper


BibTeX
@InProceedings{pmlr-v9-riihimaki10a, title = {Gaussian processes with monotonicity information}, author = {Riihimäki, Jaakko and Vehtari, Aki}, booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, pages = {645--652}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/riihimaki10a/riihimaki10a.pdf}, url = { http://proceedings.mlr.press/v9/riihimaki10a.html }, abstract = {A method for using monotonicity information in multivariate Gaussian process regression and classification is proposed. Monotonicity information is introduced with virtual derivative observations, and the resulting posterior is approximated with expectation propagation. Behaviour of the method is illustrated with artificial regression examples, and the method is used in a real world health care classification problem to include monotonicity information with respect to one of the covariates.} }
Endnote
%0 Conference Paper %T Gaussian processes with monotonicity information %A Jaakko Riihimäki %A Aki Vehtari %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-riihimaki10a %I PMLR %P 645--652 %U http://proceedings.mlr.press/v9/riihimaki10a.html %V 9 %X A method for using monotonicity information in multivariate Gaussian process regression and classification is proposed. Monotonicity information is introduced with virtual derivative observations, and the resulting posterior is approximated with expectation propagation. Behaviour of the method is illustrated with artificial regression examples, and the method is used in a real world health care classification problem to include monotonicity information with respect to one of the covariates.
RIS
TY - CPAPER TI - Gaussian processes with monotonicity information AU - Jaakko Riihimäki AU - Aki Vehtari BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-riihimaki10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 645 EP - 652 L1 - http://proceedings.mlr.press/v9/riihimaki10a/riihimaki10a.pdf UR - http://proceedings.mlr.press/v9/riihimaki10a.html AB - A method for using monotonicity information in multivariate Gaussian process regression and classification is proposed. Monotonicity information is introduced with virtual derivative observations, and the resulting posterior is approximated with expectation propagation. Behaviour of the method is illustrated with artificial regression examples, and the method is used in a real world health care classification problem to include monotonicity information with respect to one of the covariates. ER -
APA
Riihimäki, J. & Vehtari, A.. (2010). Gaussian processes with monotonicity information. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:645-652 Available from http://proceedings.mlr.press/v9/riihimaki10a.html .

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