Necessary and sufficient conditions for convergence of low-intensity Poisson–Voronoi diagrams to a unique ideal tessellation, applied to symmetric spaces and Diestel–Leader graphs.
Mikhail Gromov.Metric Structures for Riemannian and Non-Riemannian Spaces
2 Pith papers cite this work, alongside 176 external citations. Polarity classification is still indexing.
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DMW is a scalable Wasserstein statistic over random distance-matrix laws that provably lower-bounds and converges to Gromov–Wasserstein.
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Convergence towards Ideal Poisson--Voronoi tessellations with a focus on Diestel--Leader graphs
Necessary and sufficient conditions for convergence of low-intensity Poisson–Voronoi diagrams to a unique ideal tessellation, applied to symmetric spaces and Diestel–Leader graphs.
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Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning
DMW is a scalable Wasserstein statistic over random distance-matrix laws that provably lower-bounds and converges to Gromov–Wasserstein.