Learning Gaussian DAG Models without Condition Number Bounds

TL;DR

This paper introduces a new algorithm for learning Gaussian DAG models that does not depend on the condition number of the covariance matrix, making it more practical for high-dimensional data.

Contribution

The authors develop an algorithm with sample complexity independent of the condition number and establish nearly tight bounds, advancing understanding of Gaussian DAG model learning.

Findings

Sample complexity is independent of the condition number.

Lower bounds nearly match the upper bounds, confirming optimality.

Simulations validate theoretical predictions.

Abstract

We study the problem of learning the topology of a directed Gaussian Graphical Model under the equal-variance assumption, where the graph has $n$ nodes and maximum in-degree $d$. Prior work has established that $O(d \log n)$ samples are sufficient for this task. However, an important factor that is often overlooked in these analyses is the dependence on the condition number of the covariance matrix of the model. Indeed, all algorithms from prior work require a number of samples that grows polynomially with this condition number. In many cases this is unsatisfactory, since the condition number could grow polynomially with $n$, rendering these prior approaches impractical in high-dimensional settings. In this work, we provide an algorithm that recovers the underlying graph and prove that the number of samples required is independent of the condition number. Furthermore, we establish lower bounds that nearly match the upper bound up to a $d$-factor, thus providing an almost tight characterization of the true sample complexity of the problem. Moreover, under a further assumption that all the variances of the variables are bounded, we design a polynomial-time algorithm that recovers the underlying graph, at the cost of an additional polynomial dependence of the sample complexity on $d$. We complement our theoretical findings with simulations on synthetic datasets that confirm our predictions.