Graphs representing complex systems often share a partial underlying structure across
domains while retaining individual features. Thus, identifying common structures can shed …

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 …

Network estimation has been a critical component of high-dimensional data analysis and
can provide an understanding of the underlying complex dependence structures. Among the …

This paper focuses on the differential network analysis between two Gaussian graphical
models (GGMs). We introduce a new framework for inferring the structural differences …

Sparse and outlier-robust Principal Component Analysis (PCA) has been a very active field
of research recently. Yet, most existing methods apply PCA to a single dataset whereas multi …

The Gaussian graphical model (GGM) incorporates an undirected graph to represent the
conditional dependence between variables, with the precision matrix encoding partial …

Compositional data arise in many areas of research in the natural and biomedical sciences.
One prominent example is in the study of the human gut microbiome, where one can …

Hierarchical learning of generative models is useful for representing and interpreting
complex data. For instance, one application is to learn an HMM to represent an individual's …

Modern multivariate machine learning and statistical methodologies estimate parameters of
interest while leveraging prior knowledge of the association between outcome variables …