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Bibliographic Details
Main Authors: Datar, Adwait, Ay, Nihat
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2503.24022
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Table of Contents:
  • We introduce a new version of the KL-divergence for Gaussian distributions which is based on Wasserstein geometry and referred to as WKL-divergence. We show that this version is consistent with the geometry of the sample space ${\Bbb R}^n$. In particular, we can evaluate the WKL-divergence of the Dirac measures concentrated in two points which turns out to be proportional to the squared distance between these points.