Statistical models and probabilistic methods on Riemannian manifolds

01/26/2021 ∙ by Salem Said, et al. ∙ 0

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.



There are no comments yet.


page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.