20 citations · 128 across the 39 of their papers we have counts for
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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…
Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis
Sloan Nietert, Rachel Cummings, Ziv Goldfeld
The Wasserstein distance, rooted in optimal transport (OT) theory, is a popular discrepancy measure between probability distributions with various applications to statistics and ma…
Neural Estimation of Statistical Divergences
Sreejith Sreekumar, Ziv Goldfeld
Statistical divergences (SDs), which quantify the dissimilarity between probability distributions, are a basic constituent of statistical inference and machine learning. A modern m…
Limit Distribution Theory for the Smooth 1-Wasserstein Distance with Applications
Ritwik Sadhu, Ziv Goldfeld, Kengo Kato
The smooth 1-Wasserstein distance (SWD) was recently proposed as a means to mitigate the curse of dimensionality in empirical approximation while preserving the Wasserstein…
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…
Smooth -Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications
Sloan Nietert, Ziv Goldfeld, Kengo Kato
Discrepancy measures between probability distributions, often termed statistical distances, are ubiquitous in probability theory, statistics and machine learning. To combat the cur…