Understanding how unlabelled graphs depend on input values or vectors is of extreme
interest in a range of applications. In this paper, we propose a regression model taking …

The distribution function is essential in statistical inference and connected with samples to
form a directed closed loop by the correspondence theorem in measure theory and the …

In this paper, we study variational point-obstacle avoidance problems on complete
Riemannian manifolds. The problem consists of minimizing an energy functional depending …

Many problems in medicine are inherently dynamic processes which include the aspect of
change over time, such as childhood development, aging, and disease progression. From …

We propose regression models for curve-valued responses in two or more dimensions,
where only the image but not the parametrization of the curves is of interest. Examples of …

Deformable image registration and regression are important tasks in medical image
analysis. However, they are computationally expensive, especially when analyzing large …

Dynamic subspace estimation, or subspace tracking, is a fundamental problem in statistical
signal processing and machine learning. This paper considers a geodesic model for time …

Discovering and tracking communities in time-varying networks is an important task in
network science, motivated by applications in fields ranging from neuroscience to sociology …

This paper studies robust regression for data on Riemannian manifolds. Geodesic
regression is the generalization of linear regression to a setting with a manifold-valued …

We consider the topic of multivariate regression on manifold-valued output, that is, for a
multivariate observation, its output response lies on a manifold. Moreover, we propose a …