HELM: Highly Efficient Learning of Mixed copula networks

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.