1 citations · 1 across the 11 of their papers we have counts for
12 papers
Heterogeneity-calibrated Byzantine-robust distributed composite quantile regression
Xiaofei Wu, Jian Qing Shi
We study sparse composite quantile regression (CQR) for distributed data with heterogeneous honest sites and Byzantine workers. Honest sites share a common slope but may differ in…
A Prediction--Correction Analysis of Two-Way Block Splitting in Distributed Learning
Xiaofei Wu
This note revisits the convergence of the Parikh--Boyd two-way block-splitting algorithm for large-scale distributed learning through the He--Yuan prediction--correction framework.…
Distributed Stochastic Smoothing ADMM for Penalized Quantile Regression
Rongmei Liang, Xiaofei Wu
Quantile regression is well suited to heterogeneous and heavy-tailed data, but computation becomes challenging for large, distributed data sets because the check loss is nonsmooth.…
Data-Driven Pinball-Loss Selection for Vertically Distributed Elastic-Net SVMs
Xiaofei Wu, Kai Qi, Rongmei Liang
The pinball-loss support vector machine is robust, but its asymmetry parameter is usually fixed in advance. We propose a data-driven elastic-net support vector machine that learns…
Parallel Algorithms for Structured Sparse Support Vector Machines: Application in Music Genre Classification
Rongmei Liang, Zizheng Liu, Xiaofei Wu +1
Mathematical modelling, particularly through approaches such as structured sparse support vector machines (SS-SVM), plays a crucial role in processing data with complex feature str…
Feature splitting parallel algorithm for Dantzig selectors
Xiaofei Wu, Yue Chao, Rongmei Liang +2
The Dantzig selector is a widely used and effective method for variable selection in ultra-high-dimensional data. Feature splitting is an efficient processing technique that involv…
Leveraging Noisy Manual Labels as Useful Information: An Information Fusion Approach for Enhanced Variable Selection in Penalized Logistic Regression
Xiaofei Wu, Rongmei Liangse
In large-scale supervised learning, penalized logistic regression (PLR) effectively mitigates overfitting through regularization, yet its performance critically depends on robust v…
Parallel ADMM Algorithm with Gaussian Back Substitution for High-Dimensional Quantile Regression and Classification
Xiaofei Wu, Dingzi Guo, Rongmei Liang +1
In the field of high-dimensional data analysis, modeling methods based on quantile loss function are highly regarded due to their ability to provide a comprehensive statistical per…
A Partition-insensitive Parallel Framework for Distributed Model Fitting
Xiaofei Wu, Rongmei Liang, Fabio Roli +2
Distributed model fitting refers to the process of fitting a mathematical or statistical model to the data using distributed computing resources, such that computing tasks are divi…
A unified consensus-based parallel ADMM algorithm for high-dimensional regression with combined regularizations
Xiaofei Wu, Zhimin Zhang, Zhenyu Cui
The parallel alternating direction method of multipliers (ADMM) algorithm is widely recognized for its effectiveness in handling large-scale datasets stored in a distributed manner…
Multi-block linearized alternating direction method for sparse fused Lasso modeling problems
Xiaofei Wu, Rongmei Liang, Zhimin Zhang +1
In many statistical modeling problems, such as classification and regression, it is common to encounter sparse and blocky coefficients. Sparse fused Lasso is specifically designed…
Partition-Insensitive Parallel ADMM Algorithm for High-dimensional Linear Models
Xiaofei Wu, Jiancheng Jiang, Zhimin Zhang
The parallel alternating direction method of multipliers (ADMM) algorithms have gained popularity in statistics and machine learning due to their efficient handling of large sample…