20 citations · 192 across the 51 of their papers we have counts for
9 papers · 1 filter
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 low-level neural computation through interchange interven…
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…
Neural Estimation for Scaling Entropic Multimarginal Optimal Transport
Dor Tsur, Ziv Goldfeld, Kristjan Greenewald +1
Multimarginal optimal transport (MOT) is a powerful framework for modeling interactions between multiple distributions, yet its applicability is bottlenecked by a high computationa…
Robust Distribution Learning with Local and Global Adversarial Corruptions
Sloan Nietert, Ziv Goldfeld, Soroosh Shafiee
We consider learning in an adversarial environment, where an -fraction of samples from a distribution are arbitrarily modified (global corruptions) and the remaini…
Information-Theoretic Generalization Bounds for Deep Neural Networks
Haiyun He, Ziv Goldfeld
Deep neural networks (DNNs) exhibit an exceptional capacity for generalization in practical applications. This work aims to capture the effect and benefits of depth for supervised…
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…
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…
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…
Estimating Information Flow in Deep Neural Networks
Ziv Goldfeld, Ewout van den Berg, Kristjan Greenewald +4
We study the flow of information and the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the informat…