Learning DAGs without imposing acyclicity

Code

• github.com/gherardovarando/nodag
  OfficialIn papernone★ 3
• github.com/gherardovarando/nodag_experiments
  OfficialIn papernone★ 1

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 _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.

Reproductions