High-Dimensional Poisson DAG Model Learning Using $\ell_1$-Regularized Regression

5 Oct 2018 Gunwoong Park Sion Park

In this paper, we develop a new approach to learning high-dimensional Poisson directed acyclic graphical (DAG) models from only observational data without strong assumptions such as faithfulness and strong sparsity. A key component of our method is to decouple the ordering estimation or parent search where the problems can be efficiently addressed using $\ell_1$-regularized regression and the mean-variance relationship... (read more)

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