Inferring graph structure from observations on the nodes is an important and popular
network science task. Departing from the more common inference of a single graph, we …

We propose a novel algorithm for the support estimation of partially known Gaussian
graphical models that incorporates prior information about the underlying graph. In contrast …

We consider the problem of estimating the topology of multiple networks from nodal
observations, where these networks are assumed to be drawn from the same (unknown) …

We consider the problem of learning a Gaussian graphical model in the case where the
observations come from two dependent groups sharing the same variables. We focus on a …

Joint multimodal functional data acquisition, where functional data from multiple modes are
measured simultaneously from the same subject, has emerged as an exciting modern …

Gaussian graphical models are nowadays commonly applied to the comparison of groups
sharing the same variables, by jointly learning their independence structures. We consider …

We consider the problem of estimating differences in two Gaussian graphical models
(GGMs) which are known to have similar structure. The GGM structure is encoded in its …

We consider the problem of estimating the difference between two undirected functional
graphical models with shared structures. In many applications, data are naturally regarded …

Complex networked systems driven by latent inputs are common in fields like neuroscience,
finance, and engineering. A key inference problem here is to learn edge connectivity from …

Precision matrix estimation is essential in various fields, yet it is challenging when samples
for the target study are limited. Transfer learning can enhance estimation accuracy by …