A Bregman Method for Structure Learning on Sparse Directed Acyclic Graphs
Machine Learning
2020-11-06 v1 Machine Learning
Abstract
We develop a Bregman proximal gradient method for structure learning on linear structural causal models. While the problem is non-convex, has high curvature and is in fact NP-hard, Bregman gradient methods allow us to neutralize at least part of the impact of curvature by measuring smoothness against a highly nonlinear kernel. This allows the method to make longer steps and significantly improves convergence. Each iteration requires solving a Bregman proximal step which is convex and efficiently solvable for our particular choice of kernel. We test our method on various synthetic and real data sets.
Cite
@article{arxiv.2011.02764,
title = {A Bregman Method for Structure Learning on Sparse Directed Acyclic Graphs},
author = {Manon Romain and Alexandre d'Aspremont},
journal= {arXiv preprint arXiv:2011.02764},
year = {2020}
}