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

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

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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.

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arXiv Statistics ML
Aug 25

Interpretable AI with Local Distillation

Interpretable AI with Local Distillation proposes a method where a black‑box teacher model guides a regularized linear student model at each query point. The teacher defines locality by upweighting training observations with similar predicted outcomes and anchors the fit with its own prediction at the query point, treated as a pseudo‑observation. By adding Gaussian randomization and refitting, the approach identifies reliable features and stable subgroups, achieving near‑teacher accuracy while producing sparse, locally interpretable linear models.

By Erin Craig, Yiling Huang, Snigdha Panigrahi