2 citations · 3 across the 9 of their papers we have counts for
10 papers
Amplitude-Phase Analysis of the COVID-19 Point Process and the Early Countermeasures
Francesco Tripoli, Leonardo V. Santoro, Tomas Masak
We investigate how governmental restrictions relate to the spread and temporal dynamics of COVID-19 early in the pandemic. We model daily infection data from each US state as reali…
Nonparametric Riemannian Empirical Bayes, and Denoising Measurements on Manifolds
Adam Quinn Jaffe, Leonardo V. Santoro, Bodhisattva Sen
We initiate the study of nonparametric empirical Bayes denoising methods in the setting where both the latent variables and their measurements lie on a compact Riemannian manifold,…
Network Learning with Semi-relaxed Gromov-Wasserstein
Charles Dufour, Ulysse Naepels, Leonardo V. Santoro
Estimating the generative mechanism of large-scale networks is a fundamental challenge in statistical machine learning. It requires the identification of the latent connectivity st…
Likelihood Ratio Tests by Kernel Gaussian Embedding
Leonardo V. Santoro, Victor M. Panaretos
We propose a novel kernel-based nonparametric two-sample test, employing the combined use of kernel mean and kernel covariance embedding. Our test builds on recent results showing…
Kernel Embeddings and the Separation of Measure Phenomenon
Leonardo V. Santoro, Kartik G. Waghmare, Victor M. Panaretos
We prove that kernel covariance embeddings lead to information-theoretically perfect separation of distinct continuous probability distributions. In statistical terms, we establish…
Large Deviations Principle for Bures-Wasserstein Barycenters
Adam Quinn Jaffe, Leonardo V. Santoro
We prove the large deviations principle for empirical Bures-Wasserstein barycenters of independent, identically-distributed samples of covariance matrices and covariance operators.…
Statistical Inference for Bures-Wasserstein Flows
Leonardo V. Santoro, Victor M. Panaretos
We develop a statistical framework for conducting inference on collections of time-varying covariance operators (covariance flows) over a general, possibly infinite dimensional, Hi…
Large Sample Theory for Bures-Wasserstein Barycentres
Leonardo V. Santoro, Victor M. Panaretos
We establish a strong law of large numbers and a central limit theorem in the Bures-Wasserstein space of covariance operators -- or equivalently centred Gaussian measures -- over a…
A Karhunen-Loève Theorem for Random Flows in Hilbert spaces
Leonardo V. Santoro, Kartik G. Waghmare, Victor M. Panaretos
We develop a generalisation of Mercer's theorem to operator-valued kernels in infinite dimensional Hilbert spaces. We then apply our result to deduce a Karhunen-Loève theorem, vali…
Widths of crossings in Poisson Boolean percolation
Ioan Manolescu, Leonardo V. Santoro
We answer the following question: if the occupied (or vacant) set of a planar Poisson Boolean percolation model does contain a crossing of an square, how wide is this c…