no code implementations • 6 Feb 2024 • Xiaojun Mao, Hengfang Wang, Zhonglei Wang, Shu Yang
Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods.
no code implementations • 2 Jan 2024 • Weidong Liu, Xiaojun Mao, Xiaofei Zhang, Xin Zhang
To fast solve the non-smooth loss under a given privacy budget, we develop a Fast Robust And Privacy-Preserving Estimation (FRAPPE) algorithm for least absolute deviation regression.
no code implementations • 17 Jun 2023 • Jiyuan Tu, Weidong Liu, Xiaojun Mao, Mingyue Xu
The debiased estimator is a crucial tool in statistical inference for high-dimensional model parameters.
no code implementations • 20 Feb 2023 • Hengfang Wang, Yasi Zhang, Xiaojun Mao, Zhonglei Wang
Moreover, transduction with matrix completion is a useful method in multi-label learning.
no code implementations • 8 Sep 2022 • Weidong Liu, Jiyuan Tu, Xiaojun Mao, Xi Chen
Simulation studies are conducted to demonstrate the effectiveness of our proposed method.
no code implementations • 11 Feb 2022 • Weidong Liu, Xiaojun Mao, Xin Zhang
Decentralized sparsity learning has attracted a significant amount of attention recently due to its rapidly growing applications.
no code implementations • 18 Jan 2022 • 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 useless features.
no code implementations • 2 Oct 2021 • Zhengpin Li, Zheng Wei, Zengfeng Huang, Xiaojun Mao, Jian Wang
In this paper, we propose a unified framework for ensuring a strong privacy guarantee of one-bit matrix completion with DP.
no code implementations • 1 Oct 2021 • Jiabin Liu, Zheng Wei, Zhengpin Li, Xiaojun Mao, Jian Wang, Zhongyu Wei, Qi Zhang
In this work, we propose a novel and general self-adaptive module, the Self-adaptive Attention Module (SAM), which adjusts the selection bias by capturing contextual information based on its representation.
no code implementations • 1 Oct 2021 • Zheng Wei, Zhengpin Li, Xiaojun Mao, Jian Wang
Tensor completion aims at filling the missing or unobserved entries based on partially observed tensors.
no code implementations • 9 Jun 2021 • Jiayi Wang, Raymond K. W. Wong, Xiaojun Mao, Kwun Chuen Gary Chan
In particular, the proposed method achieves a stronger guarantee than existing work in terms of the scaling with respect to the observation probabilities, under asymptotically heterogeneous missing settings (where entry-wise observation probabilities can be of different orders).
no code implementations • 4 Mar 2021 • Jiyuan Tu, Weidong Liu, Xiaojun Mao, Xi Chen
Based on the proposed VRMOM estimator, we develop a general distributed inference algorithm that is robust against Byzantine failures.
no code implementations • 1 Jan 2021 • Jiyuan Tu, Weidong Liu, Xiaojun Mao
Privacy-preserving data analysis becomes prevailing in recent years.
no code implementations • 3 Dec 2020 • Zhongzheng Xiong, Jialin Sun, Xiaojun Mao, Jian Wang, Shan Ying, Zengfeng Huang
In this paper, we consider the problem of discrete distribution estimation under local differential privacy constraints.
no code implementations • ICML 2020 • 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.
no code implementations • 13 Jun 2019 • Xi Chen, Weidong Liu, Xiaojun Mao, Zhuoyi Yang
This paper studies distributed estimation and support recovery for high-dimensional linear regression model with heavy-tailed noise.
no code implementations • 19 Dec 2018 • Xiaojun Mao, Raymond K. W. Wong, Song Xi Chen
Although missing structure is a key component to any missing data problems, existing matrix completion methods often assume a simple uniform missing mechanism.