1 citations · 3 across the 10 of their papers we have counts for
3 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…
Robust and Constrained Estimation of State-Space Models: A Majorization-Minimization Approach
Yifan Yu, Shengjie Xiu, Daniel P. Palomar
In this paper, we present a novel optimization algorithm designed specifically for estimating state-space models to deal with heavy-tailed measurement noise and constraints. Our al…
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals
Andrei Buciulea, Jiaxi Ying, Antonio G. Marques +1
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 Gaussia…