316 citations · 661 across the 65 of their papers we have counts for
4 papers · 1 filter
Measuring Generalization with Optimal Transport
Ching-Yao Chuang, Youssef Mroueh, Kristjan Greenewald +2
Understanding the generalization of deep neural networks is one of the most important tasks in deep learning. Although much progress has been made, theoretical error bounds still o…
k-Mixup Regularization for Deep Learning via Optimal Transport
Kristjan Greenewald, Anming Gu, Mikhail Yurochkin +2
Mixup is a popular regularization technique for training deep neural networks that improves generalization and increases robustness to certain distribution shifts. It perturbs inpu…
Improving Approximate Optimal Transport Distances using Quantization
Gaspard Beugnot, Aude Genevay, Kristjan Greenewald +1
Optimal transport (OT) is a popular tool in machine learning to compare probability measures geometrically, but it comes with substantial computational burden. Linear programming a…
Entropic Causal Inference: Identifiability and Finite Sample Results
Spencer Compton, Murat Kocaoglu, Kristjan Greenewald +1
Entropic causal inference is a framework for inferring the causal direction between two categorical variables from observational data. The central assumption is that the amount of…