270 citations · 1k across the 102 of their papers we have counts for
13 papers · 1 filter
Human Imperceptible Attacks and Applications to Improve Fairness
Xinru Hua, Huanzhong Xu, Jose Blanchet +1
Modern neural networks are able to perform at least as well as humans in numerous tasks involving object classification and image generation. However, small perturbations which are…
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
Yiping Lu, Haoxuan Chen, Jianfeng Lu +2
In this paper, we study the statistical limits of deep learning techniques for solving elliptic partial differential equations (PDEs) from random samples using the Deep Ritz Method…
Adversarial Regression with Doubly Non-negative Weighting Matrices
Tam Le, Truyen Nguyen, Makoto Yamada +2
Many machine learning tasks that involve predicting an output response can be solved by training a weighted regression model. Unfortunately, the predictive power of this type of mo…
Statistical Analysis of Wasserstein Distributionally Robust Estimators
Jose Blanchet, Karthyek Murthy, Viet Anh Nguyen
We consider statistical methods which invoke a min-max distributionally robust formulation to extract good out-of-sample performance in data-driven optimization and learning proble…
Testing Group Fairness via Optimal Transport Projections
Nian Si, Karthyek Murthy, Jose Blanchet +1
We present a statistical testing framework to detect if a given machine learning classifier fails to satisfy a wide range of group fairness notions. The proposed test is a flexible…
Distributionally Robust Martingale Optimal Transport
Zhengqing Zhou, Jose Blanchet, Peter W. Glynn
We study the problem of bounding path-dependent expectations (within any finite time horizon ) over the class of discrete-time martingales whose marginal distributions lie withi…