Sparse Learning

Robust Personalized Federated Learning with Sparse Penalization

Federated learning (FL) is an emerging topic due to its advantage in collaborative learning with distributed data. Due to the …

Weidong Liu, Xiaojun Mao, Xiaofei Zhang, Xin Zhang

Efficient Sparse Least Absolute Deviation Regression with Differential Privacy

In recent years, privacy-preserving machine learning algorithms have attracted increasing attention because of their important …

Weidong Liu, Xiaojun Mao, Xiaofei Zhang, Xin Zhang

Clustered coefficient regression models for Poisson process with an application to seasonal warranty claim data

Motivated by a product warranty claims data set, we propose clustered coefficient regression models in a non-homogeneous Poisson …

Xin Wang, Xin Zhang, Zhengyuan Zhu

Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approach

Decentralized sparsity learning has attracted a significant amount of attention recently due to its rapidly growing applications. In this paper, we proposed a decentralized surrogate median regression (deSMR) method for efficiently solving the decentralized sparsity learning problem.

Weidong Liu, Xiaojun Mao, Xin Zhang