50 citations · 68 across the 10 of their papers we have counts for
4 papers · 1 filter
Time-Varying Graph Learning for Data with Heavy-Tailed Distribution
Amirhossein Javaheri, Jiaxi Ying, Daniel P. Palomar +1
Graph models provide efficient tools to capture the underlying structure of data defined over networks. Many real-world network topologies are subject to change over time. Learning…
Learning Large-Scale MTP Gaussian Graphical Models via Bridge-Block Decomposition
Xiwen Wang, Jiaxi Ying, Daniel P. Palomar
This paper studies the problem of learning the large-scale Gaussian graphical models that are multivariate totally positive of order two (). By introducing the concep…
Network Topology Inference with Sparsity and Laplacian Constraints
Jiaxi Ying, Xi Han, Rui Zhou +2
We tackle the network topology inference problem by utilizing Laplacian constrained Gaussian graphical models, which recast the task as estimating a precision matrix in the form of…
Algorithms for Learning Graphs in Financial Markets
José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel Perez Palomar
In the past two decades, the field of applied finance has tremendously benefited from graph theory. As a result, novel methods ranging from asset network estimation to hierarchical…