The Gaussian Kernel on the Circle and Spaces that Admit Isometric Embeddings of the Circle. - Nathaël Da Costa; Cyrus Mostajeran; Juan-Pablo Ortega (GSI (1), 2023)

Geometric Learning with Positively Decomposable Kernels. - Nathaël Da Costa; Cyrus Mostajeran; Juan-Pablo Ortega; Salem Said (CoRR, 2023)

Invariant kernels on Riemannian symmetric spaces: a harmonic-analytic approach. - Nathaël Da Costa; Cyrus Mostajeran; Juan-Pablo Ortega; Salem Said (CoRR, 2023)

Sample Path Regularity of Gaussian Processes from the Covariance Kernel. - Nathaël Da Costa; Marvin Pförtner; Lancelot Da Costa; Philipp Hennig (CoRR, 2023)

Geometric statistics with subspace structure preservation for SPD matrices. - Cyrus Mostajeran; Nathaël Da Costa; Graham W. Van Goffrier; Rodolphe Sepulchre (CoRR, 2024)

Differential Geometry with Extreme Eigenvalues in the Positive Semidefinite Cone. - Cyrus Mostajeran; Nathaël Da Costa; Graham W. Van Goffrier; Rodolphe Sepulchre (SIAM J. Matrix Anal. Appl., 2024)

Rethinking Approximate Gaussian Inference in Classification. - Bálint Mucsányi; Nathaël Da Costa; Philipp Hennig (CoRR, 2025)

Geometric Gaussian Approximations of Probability Distributions. - Nathaël Da Costa; Bálint Mucsányi; Philipp Hennig (CoRR, 2025)