11 papers
Performance Guarantees for Quantum Neural Estimation of Entropies
Sreejith Sreekumar, Ziv Goldfeld, Mark M. Wilde
Estimating quantum entropies and divergences is an important problem in quantum physics, information theory, and machine learning. Quantum neural estimators (QNEs), which utilize a…
Sliced Inner Product Gromov-Wasserstein Distances
Xiaoyun Gong, Gabriel Rioux, Ziv Goldfeld
The Gromov-Wasserstein (GW) problem provides a framework for aligning heterogeneous datasets by matching their intrinsic geometry, but its statistical and computational scaling rem…
PLOT: Progressive Localization via Optimal Transport in Neural Causal Abstraction
Jonathn Chang, Arya Datla, Ziv Goldfeld
Causal abstraction offers a principled framework for mechanistic interpretability, aligning a high-level causal model with the low-level computation realized by a neural network th…
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…
Estimation of Stochastic Optimal Transport Maps
Sloan Nietert, Ziv Goldfeld
The optimal transport (OT) map is a geometry-driven transformation between high-dimensional probability distributions which underpins a wide range of tasks in statistics, applied p…
Optimal Transportation and Alignment Between Gaussian Measures
Sanjit Dandapanthula, Aleksandr Podkopaev, Shiva Prasad Kasiviswanathan +2
Optimal transport (OT) and Gromov-Wasserstein (GW) alignment provide interpretable geometric frameworks for comparing, transforming, and aggregating heterogeneous datasets -- tasks…