20 citations · 192 across the 51 of their papers we have counts for
9 papers · 1 filter
Neural Entropic Optimal Transport and Gromov-Wasserstein Alignment
Tao Wang, Ziv Goldfeld
Optimal transport (OT) and Gromov-Wasserstein (GW) alignment are powerful frameworks for geometrically driven matching of probability distributions, yet their large-scale usage is…
Outlier-Robust Wasserstein DRO
Sloan Nietert, Ziv Goldfeld, Soroosh Shafiee
Distributionally robust optimization (DRO) is an effective approach for data-driven decision-making in the presence of uncertainty. Geometric uncertainty due to sampling or localiz…
Max-Sliced Mutual Information
Dor Tsur, Ziv Goldfeld, Kristjan Greenewald
Quantifying the dependence between high-dimensional random variables is central to statistical learning and inference. Two classical methods are canonical correlation analysis (CCA…
Quantum Neural Estimation of Entropies
Ziv Goldfeld, Dhrumil Patel, Sreejith Sreekumar +1
Entropy measures quantify the amount of information and correlation present in a quantum system. In practice, when the quantum state is unknown and only copies thereof are availabl…
Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems
Theshani Nuradha, Ziv Goldfeld, Mark M. Wilde
We propose a versatile privacy framework for quantum systems, termed quantum pufferfish privacy (QPP). Inspired by classical pufferfish privacy, our formulation generalizes and add…
Entropic Gromov-Wasserstein Distances: Stability and Algorithms
Gabriel Rioux, Ziv Goldfeld, Kengo Kato
The Gromov-Wasserstein (GW) distance quantifies discrepancy between metric measure spaces and provides a natural framework for aligning heterogeneous datasets. Alas, as exact compu…
Stability and statistical inference for semidiscrete optimal transport maps
Ritwik Sadhu, Ziv Goldfeld, Kengo Kato
We study statistical inference for the optimal transport (OT) map (also known as the Brenier map) from a known absolutely continuous reference distribution onto an unknown finitely…
Robust Estimation under the Wasserstein Distance
Sloan Nietert, Rachel Cummings, Ziv Goldfeld
We study the problem of robust distribution estimation under the Wasserstein distance, a popular discrepancy measure between probability distributions rooted in optimal transport (…
Data-Driven Optimization of Directed Information over Discrete Alphabets
Dor Tsur, Ziv Aharoni, Ziv Goldfeld +1
Directed information (DI) is a fundamental measure for the study and analysis of sequential stochastic models. In particular, when optimized over input distributions it characteriz…