Categorical and geometric methods in statistical, manifold, and machine learning
Machine Learning
2025-05-08 v1 Machine Learning
Category Theory
Differential Geometry
Statistics Theory
Statistics Theory
Abstract
We present and discuss applications of the category of probabilistic morphisms, initially developed in \cite{Le2023}, as well as some geometric methods to several classes of problems in statistical, machine and manifold learning which shall be, along with many other topics, considered in depth in the forthcoming book \cite{LMPT2024}.
Keywords
Cite
@article{arxiv.2505.03862,
title = {Categorical and geometric methods in statistical, manifold, and machine learning},
author = {Hông Vân Lê and Hà Quang Minh and Frederic Protin and Wilderich Tuschmann},
journal= {arXiv preprint arXiv:2505.03862},
year = {2025}
}
Comments
37 p., will appear as part of a special volume in the Springer Tohoku Series in Mathematics