41 citations · 111 across the 29 of their papers we have counts for
10 papers · 1 filter
On Excess Risk Convergence Rates of Neural Network Classifiers
Hyunouk Ko, Namjoon Suh, Xiaoming Huo
The recent success of neural networks in pattern recognition and classification problems suggests that neural networks possess qualities distinct from other more classical classifi…
Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks
Tian-Yi Zhou, Xiaoming Huo
This paper studies the binary classification of unbounded data from generated under Gaussian Mixture Models (GMMs) using deep ReLU neural networks. We obtain $\unic…
Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning
Yiling Xie, Xiaoming Huo
We propose an adjusted Wasserstein distributionally robust estimator -- based on a nonlinear transformation of the Wasserstein distributionally robust (WDRO) estimator in statistic…
Covariance Estimators for the ROOT-SGD Algorithm in Online Learning
Yiling Luo, Xiaoming Huo, Yajun Mei
Online learning naturally arises in many statistical and machine learning problems. The most widely used methods in online learning are stochastic first-order algorithms. Among thi…
Learning Ability of Interpolating Deep Convolutional Neural Networks
Tian-Yi Zhou, Xiaoming Huo
It is frequently observed that overparameterized neural networks generalize well. Regarding such phenomena, existing theoretical work mainly devotes to linear settings or fully-con…
The Directional Bias Helps Stochastic Gradient Descent to Generalize in Kernel Regression Models
Yiling Luo, Xiaoming Huo, Yajun Mei
We study the Stochastic Gradient Descent (SGD) algorithm in nonparametric statistics: kernel regression in particular. The directional bias property of SGD, which is known in the l…