322 citations · 1.3k across the 48 of their papers we have counts for
6 papers · 1 filter
Limit distribution theory for -Divergences
Sreejith Sreekumar, Ziv Goldfeld, Kengo Kato
-divergences, which quantify discrepancy between probability distributions, are ubiquitous in information theory, machine learning, and statistics. While there are numerous meth…
Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances
Sloan Nietert, Ritwik Sadhu, Ziv Goldfeld +1
Sliced Wasserstein distances preserve properties of classic Wasserstein distances while being more scalable for computation and estimation in high dimensions. The goal of this work…
Limit Theorems for Entropic Optimal Transport Maps and the Sinkhorn Divergence
Ziv Goldfeld, Kengo Kato, Gabriel Rioux +1
We study limit theorems for entropic optimal transport (EOT) maps, dual potentials, and the Sinkhorn divergence. The key technical tool we use is a first and second-order Hadamard…
Statistical inference with regularized optimal transport
Ziv Goldfeld, Kengo Kato, Gabriel Rioux +1
Optimal transport (OT) is a versatile framework for comparing probability measures, with many applications to statistics, machine learning, and applied mathematics. However, OT dis…
High-dimensional Data Bootstrap
Victor Chernozhukov, Denis Chetverikov, Kengo Kato +1
This article reviews recent progress in high-dimensional bootstrap. We first review high-dimensional central limit theorems for distributions of sample mean vectors over the rectan…
Limit distribution theory for smooth -Wasserstein distances
Ziv Goldfeld, Kengo Kato, Sloan Nietert +1
The Wasserstein distance is a metric on a space of probability measures that has seen a surge of applications in statistics, machine learning, and applied mathematics. However, sta…