Functional data analysis (FDA) is a fast-growing area of research and development in
statistics. While most FDA literature imposes the classical L 2 Hilbert structure on function …

Identifying gene expression differences in heterogeneous tissues across conditions is a
fundamental biological task, enabled by multi-condition single-cell RNA sequencing (RNA …

The paper develops a general regression framework for the analysis of manifold‐valued
response in a Riemannian symmetric space (RSS) and its association with multiple …

Learning complex robot motions necessarily demands to have models that are able to
encode and retrieve full-pose trajectories when tasks are defined in operational spaces …

Biological networks are commonly used in biomedical and healthcare domains to effectively
model the structure of complex biological systems with interactions linking biological entities …

We propose a method to learn a distribution of shape trajectories from longitudinal data, ie
the collection of individual objects repeatedly observed at multiple time-points. The method …

In a number of disciplines, the data (eg, graphs, manifolds) to be analyzed are non-
Euclidean in nature. Geometric deep learning corresponds to techniques that generalize …

Gaussian process (GP) regression is a powerful tool in non-parametric regression providing
uncertainty estimates. However, it is limited to data in vector spaces. In fields such as shape …

In the realm of robotics, numerous downstream robotics tasks leverage machine learning
methods for processing, modeling, or synthesizing data. Often, this data comprises variables …

Dynamic motions of humans and robots are widely driven by posture-dependent nonlinear
interactions between their degrees of freedom. However, these dynamical effects remain …