Variable Clustering via Distributionally Robust Nodewise Regression
Machine Learning
2026-05-26 v3 Optimization and Control
Computational Finance
Portfolio Management
Statistical Finance
Abstract
We study a multi-factor block model for variable clustering and connect it to regularized subspace clustering through a distributionally robust version of nodewise regression. To solve the latter problem, we derive a convex relaxation, provide a data-driven approach for selecting the size of the robust region, and develop an ADMM algorithm for efficient implementation. We validate our method in extensive numerical studies and demonstrate its superior performance.
Cite
@article{arxiv.2212.07944,
title = {Variable Clustering via Distributionally Robust Nodewise Regression},
author = {Kaizheng Wang and Xiao Xu and Xun Yu Zhou},
journal= {arXiv preprint arXiv:2212.07944},
year = {2026}
}
Comments
ICML 2026