13 citations · 33 across the 9 of their papers we have counts for
9 papers
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…
Hilbert Curve Projection Distance for Distribution Comparison
Tao Li, Cheng Meng, Hongteng Xu +1
Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling. In this study, we propose a novel metric, called Hilbe…
Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification
Mengyu Li, Jun Yu, Hongteng Xu +1
As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for matching problems of structured data like point clouds and graphs. However,…
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.…
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…
Modern Subsampling Methods for Large-Scale Least Squares Regression
Tao Li, Cheng Meng
Subsampling methods aim to select a subsample as a surrogate for the observed sample. As a powerful technique for large-scale data analysis, various subsampling methods are develop…
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…
Sufficient dimension reduction for classification using principal optimal transport direction
Cheng Meng, Jun Yu, Jingyi Zhang +2
Sufficient dimension reduction is used pervasively as a supervised dimension reduction approach. Most existing sufficient dimension reduction methods are developed for data with a…
More efficient approximation of smoothing splines via space-filling basis selection
Cheng Meng, Xinlian Zhang, Jingyi Zhang +2
We consider the problem of approximating smoothing spline estimators in a nonparametric regression model. When applied to a sample of size , the smoothing spline estimator can b…