32 citations · 84 across the 19 of their papers we have counts for
19 papers
Efficient Sparse Least Absolute Deviation Regression with Differential Privacy
Weidong Liu, Xiaojun Mao, Xiaofei Zhang +1
In recent years, privacy-preserving machine learning algorithms have attracted increasing attention because of their important applications in many scientific fields. However, in t…
Distributed Semi-Supervised Sparse Statistical Inference
Jiyuan Tu, Weidong Liu, Xiaojun Mao +1
The debiased estimator is a crucial tool in statistical inference for high-dimensional model parameters. However, constructing such an estimator involves estimating the high-dimens…
Transductive Matrix Completion with Calibration for Multi-Task Learning
Hengfang Wang, Yasi Zhang, Xiaojun Mao +1
Multi-task learning has attracted much attention due to growing multi-purpose research with multiple related data sources. Moreover, transduction with matrix completion is a useful…
Nonparametric augmented probability weighting with sparsity
Xin He, Xiaojun Mao, Zhonglei Wang
Nonresponse frequently arises in practice, and simply ignoring it may lead to erroneous inference. Besides, the number of collected covariates may increase as the sample size in mo…
Majority Vote for Distributed Differentially Private Sign Selection
Weidong Liu, Jiyuan Tu, Xiaojun Mao +1
Privacy-preserving data analysis has become more prevalent in recent years. In this study, we propose a distributed group differentially private Majority Vote mechanism, for the si…
Functional Calibration under Non-Probability Survey Sampling
Zhonglei Wang, Xiaojun Mao, Jae Kwang Kim
Non-probability sampling is prevailing in survey sampling, but ignoring its selection bias leads to erroneous inferences. We offer a unified nonparametric calibration method to est…
Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approach
Weidong Liu, Xiaojun Mao, Xin Zhang
Decentralized sparsity learning has attracted a significant amount of attention recently due to its rapidly growing applications. To obtain the robust and sparse estimators, a natu…
Nonparametric Feature Selection by Random Forests and Deep Neural Networks
Xiaojun Mao, Liuhua Peng, Zhonglei Wang
Random forests are a widely used machine learning algorithm, but their computational efficiency is undermined when applied to large-scale datasets with numerous instances and usele…
SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System
Jiabin Liu, Zheng Wei, Zhengpin Li +4
Recently, textual information has been proved to play a positive role in recommendation systems. However, most of the existing methods only focus on representation learning of text…
One-Bit Matrix Completion with Differential Privacy
Zhengpin Li, Zheng Wei, Zengfeng Huang +2
As a prevailing collaborative filtering method for recommendation systems, one-bit matrix completion requires data collected by users to provide personalized service. Due to insidi…
Applying Differential Privacy to Tensor Completion
Zheng Wei, Zhengpin Li, Xiaojun Mao +1
Tensor completion aims at filling the missing or unobserved entries based on partially observed tensors. However, utilization of the observed tensors often raises serious privacy c…
Matrix Completion with Model-free Weighting
Jiayi Wang, Raymond K. W. Wong, Xiaojun Mao +1
In this paper, we propose a novel method for matrix completion under general non-uniform missing structures. By controlling an upper bound of a novel balancing error, we construct…
Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
Jiyuan Tu, Weidong Liu, Xiaojun Mao +1
This paper develops an efficient distributed inference algorithm, which is robust against a moderate fraction of Byzantine nodes, namely arbitrary and possibly adversarial machines…
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy
Zhongzheng Xiong, Jialin Sun, Xiaojun Mao +3
Recent years, local differential privacy (LDP) has been adopted by many web service providers like Google \cite{erlingsson2014rappor}, Apple \cite{apple2017privacy} and Microsoft \…
Median Matrix Completion: from Embarrassment to Optimality
Weidong Liu, Xiaojun Mao, Raymond K. W. Wong
In this paper, we consider matrix completion with absolute deviation loss and obtain an estimator of the median matrix. Despite several appealing properties of median, the non-smoo…
Matrix Completion for Survey Data Prediction with Multivariate Missingness
Xiaojun Mao, Zhonglei Wang, Shu Yang
The National Health and Nutrition Examination Survey (NHANES) studies the nutritional and health status over the whole U.S. population with comprehensive physical examinations and…
Adjusting for Spatial Effects in Genomic Prediction
Xiaojun Mao, Somak Dutta, Raymond K. W. Wong +1
This paper investigates the problem of adjusting for spatial effects in genomic prediction. Despite being seldomly considered in genomic prediction, spatial effects often affect ph…
Distributed High-dimensional Regression Under a Quantile Loss Function
Xi Chen, Weidong Liu, Xiaojun Mao +1
This paper studies distributed estimation and support recovery for high-dimensional linear regression model with heavy-tailed noise. To deal with heavy-tailed noise whose variance…
Matrix Completion under Low-Rank Missing Mechanism
Xiaojun Mao, Raymond K. W. Wong, Song Xi Chen
Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component…