6 citations · 20 across the 19 of their papers we have counts for
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Discrete Gromov-Wasserstein Duality: Algorithms and Isomorphism Testing
Gabriel Rioux, Joanna Marks, Riccardo Passeggeri +1
The Gromov-Wasserstein (GW) distance provides a principled framework for aligning metric measure (mm) spaces based solely on their intrinsic structure. Its ability to identify isom…
Robust Alignment via Partial Gromov-Wasserstein Distances
Xiaoyun Gong, Sloan Nietert, Ziv Goldfeld
The Gromov-Wasserstein (GW) problem provides a powerful framework for aligning heterogeneous datasets by matching their internal structures in a way that minimizes distortion. Howe…
Limit Laws for Gromov-Wasserstein Alignment with Applications to Testing Graph Isomorphisms
Gabriel Rioux, Ziv Goldfeld, Kengo Kato
The Gromov-Wasserstein (GW) distance enables comparing metric measure spaces based solely on their internal structure, making it invariant to isomorphic transformations. This prope…
Neural Estimation Of Entropic Optimal Transport
Tao Wang, Ziv Goldfeld
Optimal transport (OT) serves as a natural framework for comparing probability measures, with applications in statistics, machine learning, and applied mathematics. Alas, statistic…
Neural Entropic Optimal Transport and Gromov-Wasserstein Alignment
Tao Wang, Ziv Goldfeld
Optimal transport (OT) and Gromov-Wasserstein (GW) alignment are powerful frameworks for geometrically driven matching of probability distributions, yet their large-scale usage is…
Non-Asymptotic Performance Guarantees for Neural Estimation of -Divergences
Sreejith Sreekumar, Zhengxin Zhang, Ziv Goldfeld
Statistical distances (SDs), which quantify the dissimilarity between probability distributions, are central to machine learning and statistics. A modern method for estimating such…