49 citations · 96 across the 5 of their papers we have counts for
5 papers
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…
Incorporating Unlabeled Data into Distributionally Robust Learning
Charlie Frogner, Sebastian Claici, Edward Chien +1
We study a robust alternative to empirical risk minimization called distributionally robust learning (DRL), in which one learns to perform against an adversary who can choose the d…
Alleviating Label Switching with Optimal Transport
Pierre Monteiller, Sebastian Claici, Edward Chien +3
Label switching is a phenomenon arising in mixture model posterior inference that prevents one from meaningfully assessing posterior statistics using standard Monte Carlo procedure…
Hierarchical Optimal Transport for Document Representation
Mikhail Yurochkin, Sebastian Claici, Edward Chien +2
The ability to measure similarity between documents enables intelligent summarization and analysis of large corpora. Past distances between documents suffer from either an inabilit…
Dynamical Optimal Transport on Discrete Surfaces
Hugo Lavenant, Sebastian Claici, Edward Chien +1
We propose a technique for interpolating between probability distributions on discrete surfaces, based on the theory of optimal transport. Unlike previous attempts that use linear…