4 citations · 8 across the 4 of their papers we have counts for
4 papers
Probabilistic Representation and Convergence of Gromov-Wasserstein Gradient Flows
Venkatkrishna Karumanchi, Ziv Goldfeld, Kengo Kato +1
Wasserstein gradient flows are intimately connected with evolution partial differential equations and diffusion processes. We take the first step in developing such connections for…
Gradient Flows and Riemannian Structure in the Gromov-Wasserstein Geometry
Zhengxin Zhang, Ziv Goldfeld, Kristjan Greenewald +2
The Wasserstein space of probability measures is known for its intricate Riemannian structure, which underpins the Wasserstein geometry and enables gradient flow algorithms. Howeve…
Gromov-Wasserstein Distances: Entropic Regularization, Duality, and Sample Complexity
Zhengxin Zhang, Ziv Goldfeld, Youssef Mroueh +1
The Gromov-Wasserstein (GW) distance, rooted in optimal transport (OT) theory, quantifies dissimilarity between metric measure spaces and provides a framework for aligning heteroge…
Cycle Consistent Probability Divergences Across Different Spaces
Zhengxin Zhang, Youssef Mroueh, Ziv Goldfeld +1
Discrepancy measures between probability distributions are at the core of statistical inference and machine learning. In many applications, distributions of interest are supported…