The paradigm of machine learning and artificial intelligence has pervaded our everyday life
in such a way that it is no longer an area for esoteric academics and scientists putting their …

Investment decisions and risk management require understanding the time-varying
dependencies between stocks. Graph-based learning systems have emerged as a …

Graph signal processing (GSP) is a key tool for satisfying the growing demand for
information processing over networks. However, the success of GSP in downstream learning …

We investigate the problem of learning an undirected, weighted bipartite graph under the
Gaussian Markov random field model, for which we present an optimization formulation …

We develop online graph learning algorithms from streaming network data. Our goal is to
track the (possibly) time-varying network topology, and affect memory and computational …

This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph
structures from nodal signals. Our key contribution lies in modeling the signals as Gaussian …

In this work we seek to enhance the frameworks practitioners in asset management and
wealth management may adopt to asses how different screening rules may influence the …

The growing success of graph signal processing (GSP) approaches relies heavily on prior
identification of a graph over which network data admit certain regularity. However …

Learning a graph from data is the key to taking advantage of graph signal processing tools.
Most of the conventional algorithms for graph learning require complete data statistics …

Joint network topology inference represents a canonical problem of jointly learning multiple
graph Laplacian matrices from heterogeneous graph signals. In such a problem, a widely …