AI RESEARCH
Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning
arXiv CS.LG
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ArXi:2605.14981v1 Announce Type: new Gromo--Wasserstein (GW) distances compare graphs, shapes, and point clouds through internal distances, without requiring a common coordinate system. This invariance is powerful, but discrete GW is a nonconvex quadratic optimal transport problem and is difficult to estimate at scale. We propose \emph{Distance-Matrix Wasserstein} (DMW), a hierarchy of Wasserstein statistics comparing laws of random finite distance matrices.