activity
20182022
most citedVariance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference

12 citations · 30 across the 8 of their papers we have counts for

collaborators

12 papers

stat.ME2022

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…

stat.ME2022

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…

stat.ML202211 cited

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…

cs.LG20224 cited

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…

cs.IR2021

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…

stat.ML20211 cited

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…