Outputs (3)

Probabilistic size-and-shape functional mixed models (2024)
Presentation / Conference Contribution
Wang, F., Bharath, K., Chkrebtii, O., & Kurtek, S. (2024, December). Probabilistic size-and-shape functional mixed models. Presented at Thirty-Eighth Annual Conference on Neural Information Processing Systems, Vancouver, Canada

The reliable recovery and uncertainty quantification of a fixed effect function µ in a functional mixed model, for modelling population-and object-level variability in noisily observed functional data, is a notoriously challenging task: variations al... Read More about Probabilistic size-and-shape functional mixed models.

Topo-Geometric Analysis of Variability in Point Clouds using Persistence Landscapes (2024)
Journal Article
Matuk, J., Kurtek, S., & Bharath, K. (2024). Topo-Geometric Analysis of Variability in Point Clouds using Persistence Landscapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12), 11035-11046. https://doi.org/10.1109/TPAMI.2024.3451328

Topological data analysis provides a set of tools to uncover low-dimensional structure in noisy point clouds. Prominent amongst the tools is persistence homology, which summarizes birth-death times of homological features using data objects known as... Read More about Topo-Geometric Analysis of Variability in Point Clouds using Persistence Landscapes.

A diffusion approach to Stein's method on Riemannian manifolds (2024)
Journal Article
Le, H., Lewis, A., Bharath, K., & Fallaize, C. (2024). A diffusion approach to Stein's method on Riemannian manifolds. Bernoulli, 30(2), 1079-1104. https://doi.org/10.3150/23-bej1625

We detail an approach to developing Stein’s method for bounding integral metrics on probability measures defined on a Riemannian manifold M. Our approach exploits the relationship between the generator of a diffusion on M having a target invariant me... Read More about A diffusion approach to Stein's method on Riemannian manifolds.