3 papers
cs.LG2024
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…
eess.SP2024
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…
cs.LG2024
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…