论文标题
Bregman在稀疏定向无环图上学习结构的方法
A Bregman Method for Structure Learning on Sparse Directed Acyclic Graphs
论文作者
论文摘要
我们开发了一种在线性结构因果模型上进行结构学习的Bregman近端梯度方法。尽管该问题是非凸，但实际上是NP-HARD，但Bregman梯度方法使我们能够通过测量对高度非线性内核的平滑度来中和曲率的一部分影响。这允许该方法做出更长的步骤并显着改善收敛性。每次迭代都需要解决Bregman近端步骤，该步骤是凸的，可以有效地解决我们的特定内核选择。我们在各种合成和真实数据集上测试我们的方法。
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.
