arXiv Machine Learning July 7, 2026 By Zhen Huang, Peicheng Xu, Junbiao Pang, Yulong Zheng

Adversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning

Read the original on arXiv Machine Learning →

arXiv:2607. 03839v1 Announce Type: new Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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