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Title: Global optimality in bivariate gradient-based DAG learning
Recently, a new class of non-convex optimization problems motivated by the statistical problem of learning an acyclic directed graphical model from data has attracted significant interest. While existing work uses standard first-order optimization schemes to solve this problem, proving the global optimality of such approaches has proven elusive. The difficulty lies in the fact that unlike other non-convex problems in the literature, this problem is not "benign", and possesses multiple spurious solutions that standard approaches can easily get trapped in. In this paper, we prove that a simple path-following optimization scheme globally converges to the global minimum of the population loss in the bivariate setting.  more » « less
Award ID(s):
1956330
PAR ID:
10542250
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Main Conference Track
Date Published:
Subject(s) / Keyword(s):
nonconvex optimization global optimization homotopy graphical models directed acyclic graphs
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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