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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…