Learning DAGs without imposing acyclicity

• URL: http://arxiv.org/abs/2006.03005v1
• Date: Thu, 4 Jun 2020 16:52:01 GMT
• Title: Learning DAGs without imposing acyclicity
• Authors: Gherardo Varando
• Abstract summary: We show that it is possible to learn a directed acyclic graph (DAG) from data without imposing the acyclicity constraint. This approach is computationally efficient and is not affected by the explosion of complexity as in classical structural learning algorithms.
• Score: 0.6526824510982799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We explore if it is possible to learn a directed acyclic graph (DAG) from data without imposing explicitly the acyclicity constraint. In particular, for Gaussian distributions, we frame structural learning as a sparse matrix factorization problem and we empirically show that solving an $\ell_1$-penalized optimization yields to good recovery of the true graph and, in general, to almost-DAG graphs. Moreover, this approach is computationally efficient and is not affected by the explosion of combinatorial complexity as in classical structural learning algorithms.

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