11 citations · 41 across the 15 of their papers we have counts for
15 papers
Wasserstein Generative Regression
Shanshan Song, Tong Wang, Guohao Shen +2
In this paper, we propose a new and unified approach for nonparametric regression and conditional distribution learning. Our approach simultaneously estimates a regression function…
Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression
Guohao Shen, Yuling Jiao, Yuanyuan Lin +1
We study the properties of differentiable neural networks activated by rectified power unit (RePU) functions. We show that the partial derivatives of RePU neural networks can be re…
Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction
Wenlu Tang, Guohao Shen, Yuanyuan Lin +1
We propose a nonparametric quantile regression method using deep neural networks with a rectified linear unit penalty function to avoid quantile crossing. This penalty function is…
Deep Sufficient Representation Learning via Mutual Information
Siming Zheng, Yuanyuan Lin, Jian Huang
We propose a mutual information-based sufficient representation learning (MSRL) approach, which uses the variational formulation of the mutual information and leverages the approxi…
Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks
Guohao Shen, Yuling Jiao, Yuanyuan Lin +2
We propose a penalized nonparametric approach to estimating the quantile regression process (QRP) in a nonseparable model using rectifier quadratic unit (ReQU) activated deep neura…
Statistical Inference in High-dimensional Generalized Linear Models with Streaming Data
Lan Luo, Ruijian Han, Yuanyuan Lin +1
In this paper we develop an online statistical inference approach for high-dimensional generalized linear models with streaming data for real-time estimation and inference. We prop…
Deep Quantile Regression: Mitigating the Curse of Dimensionality Through Composition
Guohao Shen, Yuling Jiao, Yuanyuan Lin +2
This paper considers the problem of nonparametric quantile regression under the assumption that the target conditional quantile function is a composition of a sequence of low-dimen…
Robust Nonparametric Regression with Deep Neural Networks
Guohao Shen, Yuling Jiao, Yuanyuan Lin +1
In this paper, we study the properties of robust nonparametric estimation using deep neural networks for regression models with heavy tailed error distributions. We establish the n…
Combining case-control studies for identifiability and efficiency improvement in logistic regression
Wenlu Tang, Yuanyuan Lin, Linlin Dai +1
Can two separate case-control studies, one about Hepatitis disease and the other about Fibrosis, for example, be combined together? It would be hugely beneficial if two or more sep…
Online Debiased Lasso for Streaming Data
Ruijian Han, Lan Luo, Yuanyuan Lin +1
We propose an online debiased lasso (ODL) method for statistical inference in high-dimensional linear models with streaming data. The proposed ODL consists of an efficient computat…
Non-asymptotic Excess Risk Bounds for Classification with Deep Convolutional Neural Networks
Guohao Shen, Yuling Jiao, Yuanyuan Lin +1
In this paper, we consider the problem of binary classification with a class of general deep convolutional neural networks, which includes fully-connected neural networks and fully…
Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial Prefactors
Yuling Jiao, Guohao Shen, Yuanyuan Lin +1
We study the properties of nonparametric least squares regression using deep neural networks. We derive non-asymptotic upper bounds for the prediction error of the empirical risk m…
Nearly Semiparametric Efficient Estimation of Quantile Regression
Kani Chen, Yuanyuan Lin, Zhanfeng Wang +1
As a competitive alternative to least squares regression, quantile regression is popular in analyzing heterogenous data. For quantile regression model specified for one single quan…
Regression Analysis with Response-biased Sampling
Kani Chen, Yuanyuan Lin, Yuan Yao +1
Response-biased sampling, in which samples are drawn from a popula- tion according to the values of the response variable, is common in biomedical, epidemiological, economic and so…
Least Product Relative Error Estimation
Kani Chen, Yuanyuan Lin, Zhanfeng Wang +1
A least product relative error criterion is proposed for multiplicative regression models. It is invariant under scale transformation of the outcome and covariates. In addition, th…