200 citations · 203 across the 4 of their papers we have counts for
11 papers
Stabilizing the Kumaraswamy Distribution
Max Wasserman, Gonzalo Mateos
Large-scale latent variable models require expressive continuous distributions that support efficient sampling and low-variance differentiation, achievable through the reparameteri…
Online Proximal ADMM for Graph Learning from Streaming Smooth Signals
Hector Chahuara, Gonzalo Mateos
Graph signal processing deals with algorithms and signal representations that leverage graph structures for multivariate data analysis. Often said graph topology is not readily ava…
Blind Deconvolution on Graphs: Exact and Stable Recovery
Chang Ye, Gonzalo Mateos
We study a blind deconvolution problem on graphs, which arises in the context of localizing a few sources that diffuse over networks. While the observations are bilinear functions…
Non-negative Weighted DAG Structure Learning
Samuel Rey, Seyed Saman Saboksayr, Gonzalo Mateos
We address the problem of learning the topology of directed acyclic graphs (DAGs) from nodal observations, which adhere to a linear structural equation model. Recent advances frame…
Graph Structure Learning with Interpretable Bayesian Neural Networks
Max Wasserman, Gonzalo Mateos
Graphs serve as generic tools to encode the underlying relational structure of data. Often this graph is not given, and so the task of inferring it from nodal observations becomes…
Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks
Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro
Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing brain age with respect to chronological age can reflect…