Variable Clustering via Distributionally Robust Nodewise Regression

Kaizheng Wang; Xiao Xu; Xun Yu Zhou

ICML 2026poster0 citations

Variable Clustering via Distributionally Robust Nodewise Regression

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.

Robustness

BibTeX

@inproceedings{
wang2026variable,
title={Variable Clustering via Distributionally Robust Nodewise Regression},
author={Kaizheng Wang and Xiao Xu and XUNYU ZHOU},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=FVmpIRN0N2}
}