41 citations · 111 across the 29 of their papers we have counts for
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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…
Solving a Special Type of Optimal Transport Problem by a Modified Hungarian Algorithm
Yiling Xie, Yiling Luo, Xiaoming Huo
Computing the empirical Wasserstein distance in the Wasserstein-distance-based independence test is an optimal transport (OT) problem with a special structure. This observation ins…
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…
Hot-spots Detection in Count Data by Poisson Assisted Smooth Sparse Tensor Decomposition
Yujie Zhao, Xiaoming Huo, Yajun Mei
Count data occur widely in many bio-surveillance and healthcare applications, e.g., the numbers of new patients of different types of infectious diseases from different cities/coun…
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…
Implicit Regularization Properties of Variance Reduced Stochastic Mirror Descent
Yiling Luo, Xiaoming Huo, Yajun Mei
In machine learning and statistical data analysis, we often run into objective function that is a summation: the number of terms in the summation possibly is equal to the sample si…