A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Develops log-Euclidean Lie groups for SPD and correlation matrices.
A new method compares unaligned datasets using log-Euclidean signatures of SPD matrices.
A new mechanism for differentially private Fréchet mean on SPD matrices.
Unified framework for Riemannian deep learning across manifold-valued representations.
Symmetric Positive Definite (SPD) matrices have been used in many fields of medical data analysis. Many Riemannian metrics have been defined on this manifold but the choice of the Riemannian structure lacks a set of principles that could lead one to choose properly the metric. This drives us to introduce the principle …
Python package for SPD matrix distances, reproducible and extensible.
Extends metrics for SPD matrices to infinite dimensions.
We present a new Riemannian metric, termed Log-Cholesky metric, on the manifold of symmetric positive definite (SPD) matrices via Cholesky decomposition. We first construct a Lie group structure and a bi-invariant metric on Cholesky space, the collection of lower triangular matrices whose diagonal elements are all posi…
This work improves understanding of symmetrizing Bregman divergences on positive definite matrices.
New metrics defined on SPD matrices link to divergences and curvature.
Novel unsupervised MIG detectors improve signal detection in cluttered environments.
Unified framework for optimal transport on curved spaces using neural potentials.