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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.…
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…
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…