1 citations · 1 across the 5 of their papers we have counts for
6 papers
Smooth Isotropic Covariance Functions on Metric Graphs via Polyharmonic Resistance Distances
Tobia Filosi, Emilio Porcu, Claudio Agostinelli
Metric graphs are generalisations of linear networks and provide a natural framework for the definition of continuously-indexed Gaussian processes. We define a new class of distanc…
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…
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…
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…
A regularized MANOVA test for semicontinuous high-dimensional data
Elena Sabbioni, Claudio Agostinelli, Alessio Farcomeni
We propose a MANOVA test for semicontinuous data that is applicable also when the dimensionality exceeds the sample size. The test statistic is obtained as a likelihood ratio, wher…
Temporally-Evolving Generalised Networks and their Reproducing Kernels
Tobia Filosi, Claudio Agostinelli, Emilio Porcu
This paper considers generalised network, intended as networks where (a) the edges connecting the nodes are nonlinear, and (b) stochastic processes are continuously indexed over bo…