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A1468
Title: Efficient learning of quadratic variance function directed acyclic graphs via topological layers Authors:  Wei Zhong - Xiamen University (China)
Wei Zhou - Xiamen University, City University of Hong Kong (China) [presenting]
Abstract: Directed acyclic graph (DAG) models are widely used to represent causal relationships among random variables in many application domains. A special class of non-Gaussian DAG models is studied, where the conditional variance of each node given its parents is a quadratic function of its conditional mean. Such a class of non-Gaussian DAG models are fairly flexible and admit many popular distributions as special cases, including Poisson, Binomial, Geometric, Exponential, and Gamma. To facilitate learning, we introduce a novel concept of topological layers, and develop an efficient DAG learning algorithm. It first reconstructs the topological layers in a hierarchical fashion and then recoveries the directed edges between nodes in different layers, which requires much less computational cost than most existing algorithms. Its advantage is also demonstrated in a number of simulated examples, as well as its applications to two real-life datasets, including an NBA player statistics data and cosmetic sales data collected by Alibaba.