HELM: Highly Efficient Learning of Mixed copula networks

Yaniv Tenzer Huji, Gal Elidan
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:381-390, 2014.

Abstract

Learning the structure of probabilistic graphi- cal models for complex real-valued domains is a formidable computational challenge. This in- evitably leads to significant modelling compro- mises such as discretization or the use of a sim- plistic Gaussian representation. In this work we address the challenge of efficiently learning truly expressive copula-based networks that facilitate a mix of varied copula families within the same model. Our approach is based on a simple but powerful bivariate building block that is used to highly efficiently perform local model selection, thus bypassing much of computational burden in- volved in structure learning. We show how this building block can be used to learn general net- works and demonstrate its effectiveness on var- ied and sizeable real-life domains. Importantly, favorable identification and generalization per- formance come with dramatic runtime improve- ments. Indeed, the benefits are such that they allow us to tackle domains that are prohibitive when using a standard learning approaches.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-huji14a, title = {{HELM}: Highly Efficient Learning of Mixed copula networks}, author = {Huji, Yaniv Tenzer and Elidan, Gal}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {381--390}, 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/huji14a/huji14a.pdf}, url = {https://proceedings.mlr.press/r12/huji14a.html}, abstract = {Learning the structure of probabilistic graphi- cal models for complex real-valued domains is a formidable computational challenge. This in- evitably leads to significant modelling compro- mises such as discretization or the use of a sim- plistic Gaussian representation. In this work we address the challenge of efficiently learning truly expressive copula-based networks that facilitate a mix of varied copula families within the same model. Our approach is based on a simple but powerful bivariate building block that is used to highly efficiently perform local model selection, thus bypassing much of computational burden in- volved in structure learning. We show how this building block can be used to learn general net- works and demonstrate its effectiveness on var- ied and sizeable real-life domains. Importantly, favorable identification and generalization per- formance come with dramatic runtime improve- ments. Indeed, the benefits are such that they allow us to tackle domains that are prohibitive when using a standard learning approaches.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T HELM: Highly Efficient Learning of Mixed copula networks %A Yaniv Tenzer Huji %A Gal Elidan %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-huji14a %I PMLR %P 381--390 %U https://proceedings.mlr.press/r12/huji14a.html %V R12 %X Learning the structure of probabilistic graphi- cal models for complex real-valued domains is a formidable computational challenge. This in- evitably leads to significant modelling compro- mises such as discretization or the use of a sim- plistic Gaussian representation. In this work we address the challenge of efficiently learning truly expressive copula-based networks that facilitate a mix of varied copula families within the same model. Our approach is based on a simple but powerful bivariate building block that is used to highly efficiently perform local model selection, thus bypassing much of computational burden in- volved in structure learning. We show how this building block can be used to learn general net- works and demonstrate its effectiveness on var- ied and sizeable real-life domains. Importantly, favorable identification and generalization per- formance come with dramatic runtime improve- ments. Indeed, the benefits are such that they allow us to tackle domains that are prohibitive when using a standard learning approaches. %Z Reissued by PMLR on 04 October 2026.
APA
Huji, Y.T. & Elidan, G.. (2014). HELM: Highly Efficient Learning of Mixed copula networks. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:381-390 Available from https://proceedings.mlr.press/r12/huji14a.html. Reissued by PMLR on 04 October 2026.

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