20 citations · 192 across the 51 of their papers we have counts for
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Approximation rates of entropic maps in semidiscrete optimal transport
Ritwik Sadhu, Ziv Goldfeld, Kengo Kato
Entropic optimal transport offers a computationally tractable approximation to the classical problem. In this note, we study the approximation rate of the entropic optimal transpor…
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…
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…
Robust Distribution Learning with Local and Global Adversarial Corruptions
Sloan Nietert, Ziv Goldfeld, Soroosh Shafiee
We consider learning in an adversarial environment, where an -fraction of samples from a distribution are arbitrarily modified (global corruptions) and the remaini…
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…
Information-Theoretic Generalization Bounds for Deep Neural Networks
Haiyun He, Ziv Goldfeld
Deep neural networks (DNNs) exhibit an exceptional capacity for generalization in practical applications. This work aims to capture the effect and benefits of depth for supervised…