arXiv stat.ML
· Papers
Contrast-Free ICA and Causal Inference via Wasserstein Distances to the Gaussian
arXiv:2607.12832v1 Announce Type: new Abstract: We study the squared $2$-Wasserstein distance to the standard Gaussian as a non-Gaussianity criterion and use it for linear Independent Component Analysis (ICA) and causal inference in Linear Non-Gaussian Acyclic Models (LiNGAM). The analysis relies on a strict inequality