The combinatorial problem of learning directed acyclic graphs (DAGs) from data was
recently framed as a purely continuous optimization problem by leveraging a differentiable …

Recently, an intriguing class of non-convex optimization problems has emerged in the
context of learning directed acyclic graphs (DAGs). These problems involve minimizing a …

Traditionally, Bayesian network structure learning is often carried out at a central site, in
which all data is gathered. However, in practice, data may be distributed across different …

This paper investigates in which cases continuous optimization for directed acyclic graph
(DAG) structure learning can and cannot perform well and why this happens, and suggests …

We establish finite-sample guarantees for a polynomial-time algorithm for learning a
nonlinear, nonparametric directed acyclic graphical (DAG) model from data. The analysis is …

We analyze the complexity of learning directed acyclic graphical models from observational
data in general settings without specific distributional assumptions. Our approach is …

We consider the problem of discovering $ K $ related Gaussian directed acyclic graphs
(DAGs), where the involved graph structures share a consistent causal order and sparse …

This article proposes a novel causal discovery and inference method called GrIVET for a
Gaussian directed acyclic graph with unmeasured confounders. GrIVET consists of an order …

We establish finite-sample guarantees for efficient proper learning of bounded-degree {\em
polytrees}, a rich class of high-dimensional probability distributions and a subclass of …

We study the optimal sample complexity of learning a Gaussian directed acyclic graph
(DAG) from observational data. Our main results establish the minimax optimal sample …