5 citations · 6 across the 6 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…
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…
An Accelerated Stochastic Variance-Reduced Algorithm for Entropic Wasserstein Barycenters
Yiling Xie, Yiling Luo, Xiaoming Huo
Fixed-support Wasserstein barycenters average probability distributions while accounting for the geometry of the support. We study the entropically regularized Wasserstein barycent…