Anton Mallasto

Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models

2018 · arXiv (Cornell University)

Latent variable models (LVMs) learn probabilistic models of data manifolds lying in an \emph{ambient} Euclidean space. In a number of applications, a priori known spatial constraints can shrink the ambient space into a considerably smaller …

Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals

2021 · arXiv (Cornell University)

\emph{Optimal Transport} (OT) has emerged as an important computational tool in machine learning and computer vision, providing a geometrical framework for studying probability measures. OT unfortunately suffers from the curse of dimensionality and requires regularization …