activity
20042023
most citedOptimal Subsampling for Large Sample Logistic Regression

270 citations · 537 across the 20 of their papers we have counts for

collaborators

20 papers

stat.ME2023

Leverage classifier: Another look at support vector machine

Yixin Han, Jun Yu, Nan Zhang +4

Support vector machine (SVM) is a popular classifier known for accuracy, flexibility, and robustness. However, its intensive computation has hindered its application to large-scale…

stat.ML2022

An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation

Jingyi Zhang, Cheng Meng, Jun Yu +3

Subsampling methods aim to select a subsample as a surrogate for the observed sample. Such methods have been used pervasively in large-scale data analytics, active learning, and pr…

stat.ME2021

Smoothing splines approximation using Hilbert curve basis selection

Cheng Meng, Jun Yu, Yongkai Chen +2

Smoothing splines have been used pervasively in nonparametric regressions. However, the computational burden of smoothing splines is significant when the sample size is large.…

stat.ME2021

Best Subset Selection: Statistical Computing Meets Quantum Computing

Wenxuan Zhong, Yuan Ke, Ye Wang +3

With the rapid development of quantum computers, quantum algorithms have been studied extensively. However, quantum algorithms tackling statistical problems are still lacking. In t…

stat.ML2021★ 13 cited

Large-scale optimal transport map estimation using projection pursuit

Cheng Meng, Yuan Ke, Jingyi Zhang +3

This paper studies the estimation of large-scale optimal transport maps (OTM), which is a well-known challenging problem owing to the curse of dimensionality. Existing literature a…

stat.ME2020★ 3 cited

LowCon: A design-based subsampling approach in a misspecified linear modeL

Cheng Meng, Rui Xie, Abhyuday Mandal +3

We consider a measurement constrained supervised learning problem, that is, (1) full sample of the predictors are given; (2) the response observations are unavailable and expensive…