Gaussian processes are commonly used machine learning models, capable of learning unknown functions in a way that represents uncertainty, thereby facilitating construction of optimal decision-making systems. Motivated by a desire to deploy Gaussian processes in novel areas of science, a rapidly-growing line of work has focused on constructively extending these models to handle non-Euclidean domains, including data defined on Riemannian manifolds, such as spheres and tori. In this work, we propos…