3 papers
math.ST2026
Central subspace data depth
Giacomo Francisci, Claudio Agostinelli
Statistical data depth plays an important role in the analysis of multivariate data sets. The main outcome is a center-outward ordering of the observations that can be used both to…
stat.ML2025
Hellinger loss function for Generative Adversarial Networks
Giovanni Saraceno, Anand N. Vidyashankar, Claudio Agostinelli
We propose Hellinger-type loss functions for training Generative Adversarial Networks (GANs), motivated by the boundedness, symmetry, and robustness properties of the Hellinger dis…
math.ST2025
Vector-Valued Gaussian Processes and their Kernels on a Class of Metric Graphs
Tobia Filosi, Emilio Porcu, Xavier Emery +2
Despite the increasing importance of stochastic processes on linear networks and graphs, current literature on multivariate (vector-valued) Gaussian random fields on metric graphs…