5 citations · 13 across the 6 of their papers we have counts for
14 papers
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…
Neural Estimation and Optimization of Directed Information over Continuous Spaces
Dor Tsur, Ziv Aharoni, Ziv Goldfeld +1
This work develops a new method for estimating and optimizing the directed information rate between two jointly stationary and ergodic stochastic processes. Building upon recent ad…
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…
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…
The Information Bottleneck Problem and Its Applications in Machine Learning
Ziv Goldfeld, Yury Polyanskiy
Inference capabilities of machine learning (ML) systems skyrocketed in recent years, now playing a pivotal role in various aspect of society. The goal in statistical learning is to…
The Secrecy Capacity of Cost-Constrained Wiretap Channels
Sreejith Sreekumar, Alexander Bunin, Ziv Goldfeld +2
In many information-theoretic channel coding problems, adding an input cost constraint to the operational setup amounts to restricting the optimization domain in the capacity formu…