12 citations · 34 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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 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…