Adversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning
For practitioners needing stable and robust sparse feature selection in high-dimensional settings (e.g., biomedical data), AdLNet offers a principled improvement over existing LassoNet.
Adversarial LassoNet integrates input-space adversarial perturbations with hierarchical sparsity to improve robustness and stability of sparse feature selection. It achieves 4.4% better out-of-distribution robustness, 6.3% higher feature support reproducibility, and 5.3% test accuracy gain over vanilla LassoNet.
Sparse feature selection is critical for high-dimensional machine learning, yet traditional $\ell_1$-regularized methods are often brittle under observational noise and spurious correlations, leading to unstable feature supports and degraded generalization. Although adversarial training has been widely used to improve model robustness, its interaction with hierarchical sparse feature selection remains underexplored. In this work, we propose Adversarial LassoNet (AdLNet), a stability-driven sparse feature selection framework that integrates input-space adversarial perturbations with the hierarchical sparsity mechanism of LassoNet. We derive a tractable first-order adversarial approximation under local smoothness assumptions and provide an NTK-inspired spectral analysis to characterize how perturbation-driven training can reduce gradient concentration. Experiments on high-dimensional SERS data, six public benchmark datasets, and ColoredMNIST show that AdLNet maintains competitive sparse-selection performance while improving out-of-distribution robustness by 4.4\% and feature support reproducibility by 6.3\% under nearly matched support sparsity on ColoredMNIST. On the high-dimensional lung cancer screening dataset, AdLNet achieves a 5.3\% test accuracy gain and a 6.0\% AUC improvement over vanilla LassoNet. Code and dataset are available at https://github.com/719573/Adversarial-LassoNet.