mlsanityData to 14 Oct 2026

Information Geometry for Wasserstein KL Divergence of Gaussian Measures on ℝn

arXiv 2503.24022 ↗First posted 31 Mar 2025math.ST stat.ML stat.THcited by 1

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Abstract (arXiv)

We study the Wasserstein Kullback--Leibler divergence (WKL divergence) on the manifold of nondegenerate Gaussian measures over mathbb Rn. In the canonical-divergence construction, the Fisher--Rao metric recovers forward KL along intrinsic mixture geodesics and reverse KL along geodesics of the conjugate exponential connection. Replacing the Fisher--Rao metric by the Otto metric and following the latter route produces the e1-connection underlying WKL divergence. We establish its geodesic completeness, classify its forward limits, prove that every ordered pair is joined by a unique e1-connector generated by a quadratic potential, and derive an explicit WKL divergence formula with separate mean and covariance contributions. WKL divergence is nonnegative and separating. At equal covariances WKL divergence equals one half of the squared Euclidean mean distance. Finally, WKL divergence extends finitely and continuously to singular targets from a nondegenerate source, but diverges when the target remains nondegenerate and the source covariance becomes singular. Path-dependent joint limits at Dirac pairs preclude a continuous extension to the full positive-semidefinite covariance product, although a lower-semicontinuous extended-real extension exists.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 23 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

4 of 23 references.

References · 4

  1. 2020Entropy-Regularized 2-Wasserstein Distance between Gaussian Measurescited by 32
  2. 2022When Optimal Transport Meets Information Geometrycited by 10
  3. 2017Information Geometry Connecting Wasserstein Distance and Kullback-Leibler Divergence via the Entropy-Relaxed Transportation Problemcited by 9
  4. 2021Transport information Bregman divergencescited by 3

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Citing papers per year: 2026: 1.

1 paper.