8 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…
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…
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…
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…