Uncertainty in Artificial Intelligence
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HELM: Highly Efficient Learning of Mixed copula networks
Yaniv Tenzer, Gal Elidan
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.
Keywords:
Pages: 790-799
PS Link:
PDF Link: /papers/14/p790-tenzer.pdf
BibTex:
@INPROCEEDINGS{Tenzer14,
AUTHOR = "Yaniv Tenzer and Gal Elidan",
TITLE = "HELM: Highly Efficient Learning of Mixed copula networks",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "790--799"
}


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