2 citations · 2 across the 7 of their papers we have counts for
7 papers
Universality of kernels on Riemannian symmetric spaces
Salem Said, Nathaël Da Costa, Franziskus Steinert +1
We investigate universality properties of continuous, positive-definite invariant kernels on Riemannian symmetric spaces, providing a unified harmonic-analytic characterization acr…
Constructive Disintegration and Conditional Modes
Nathaël Da Costa, Marvin Pförtner, Jon Cockayne
Conditioning, the central operation in Bayesian statistics, is formalised by the notion of disintegration of measures. However, due to the implicit nature of their definition, cons…
Geometric Gaussian Approximations of Probability Distributions
Nathaël Da Costa, Bálint Mucsányi, Philipp Hennig
Approximating complex probability distributions, such as Bayesian posterior distributions, is of central interest in many applications. We study the expressivity of geometric Gauss…
A theory of generalised coordinates for stochastic differential equations
Lancelot Da Costa, Nathaël Da Costa, Conor Heins +5
Stochastic differential equations are ubiquitous modelling tools in physics and the sciences. In most modelling scenarios, random fluctuations driving dynamics or motion have some…
Geometric statistics with subspace structure preservation for SPD matrices
Cyrus Mostajeran, Nathaël Da Costa, Graham Van Goffrier +1
We present a geometric framework for the processing of SPD-valued data that preserves subspace structures and is based on the efficient computation of extreme generalized eigenvalu…
Sample Path Regularity of Gaussian Processes from the Covariance Kernel
Nathaël Da Costa, Marvin Pförtner, Lancelot Da Costa +1
Gaussian processes (GPs) are the most common formalism for defining probability distributions over spaces of functions. While applications of GPs are myriad, a comprehensive unders…
Differential geometry with extreme eigenvalues in the positive semidefinite cone
Cyrus Mostajeran, Nathaël Da Costa, Graham Van Goffrier +1
Differential geometric approaches to the analysis and processing of data in the form of symmetric positive definite (SPD) matrices have had notable successful applications to numer…