3 papers
stat.ML2025
Colored Markov Random Fields for Probabilistic Topological Modeling
Lorenzo Marinucci, Leonardo Di Nino, Gabriele D'Acunto +3
Probabilistic Graphical Models (PGMs) encode conditional dependencies among random variables using a graph -nodes for variables, links for dependencies- and factorize the joint dis…
cs.LG2025
Learning the Structure of Connection Graphs
Leonardo Di Nino, Gabriele D'Acunto, Sergio Barbarossa +1
Connection graphs (CGs) extend traditional graph models by coupling network topology with orthogonal transformations, enabling the representation of global geometric consistency. T…
eess.SP2025
Learning Sheaf Laplacian Optimizing Restriction Maps
Leonardo Di Nino, Sergio Barbarossa, Paolo Di Lorenzo
The aim of this paper is to propose a novel framework to infer the sheaf Laplacian, including the topology of a graph and the restriction maps, from a set of data observed over the…