# Statistical models and probabilistic methods on Riemannian manifolds

This entry contains the core material of my habilitation thesis, soon to be officially submitted. It provides a self-contained presentation of the original results in this thesis, in addition to their detailed proofs. The motivation of these results is the analysis of data which lie in Riemannian manifolds. Their aim is to bring about general, meaningful, and applicable tools, which can be used to model, and to learn from such "Riemannian data", as well as to analyse the various algorithms which may be required in this kind of pursuit (for sampling, optimisation, stochastic approximation, ...). The world of Riemannian data and algorithms can be quite different from its Euclidean counterpart, and this difference is the source of mathematical problems, addressed in my thesis. The first chapter provides some taylor-made geometric constructions, to be used in the thesis, while subsequent chapters (there are four more of them), address a series of issues, which arise from unresolved challenges, in the recent literature. A one-page guide, on how to read the thesis, is to be found right after the table of contents.

## Authors

• 10 publications
• ### Riemannian Adaptive Optimization Methods

Several first order stochastic optimization methods commonly used in the...
10/01/2018 ∙ by Gary Becigneul, et al. ∙ 0

• ### Polynomial Regression on Riemannian Manifolds

In this paper we develop the theory of parametric polynomial regression ...
01/11/2012 ∙ by Jacob Hinkle, et al. ∙ 0

• ### Bayesian Quadrature on Riemannian Data Manifolds

Riemannian manifolds provide a principled way to model nonlinear geometr...
02/12/2021 ∙ by Christian Fröhlich, et al. ∙ 14

• ### Interpretable Stein Goodness-of-fit Tests on Riemannian Manifolds

In many applications, we encounter data on Riemannian manifolds such as ...
03/01/2021 ∙ by Wenkai Xu, et al. ∙ 0

• ### From Nesterov's Estimate Sequence to Riemannian Acceleration

We propose the first global accelerated gradient method for Riemannian m...
01/24/2020 ∙ by Kwangjun Ahn, et al. ∙ 0

• ### Fast, asymptotically efficient, recursive estimation in a Riemannian manifold

Stochastic optimisation in Riemannian manifolds, especially the Riemanni...
05/17/2018 ∙ by Jialun Zhou, et al. ∙ 0

• ### Evolving Algebras 1993: Lipari Guide

Computation models and specification methods seem to be worlds apart. Th...
08/19/2018 ∙ by Yuri Gurevich, et al. ∙ 0

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