Author

Junbiao Pang

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Preprint Jul 2026

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

Adversarial LassoNet is proposed, a stability-driven sparse feature selection framework that integrates input-space adversarial perturbations with the hierarchical sparsity mechanism of LassoNet and an NTK-inspired spectral analysis to characterize how perturbation-driven training can reduce gradient concentration.

Zhenghao Huang, Peicheng Xu, Junbiao Pang et al. · 0 citations

Preprint Jul 2026

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models

Efficient Tuning Before Quantization (ETBQ) is proposed, a pre-conditioning tuning stage for Stochastic Gradient Descent (SGD)-optimized models before PTQ, which improves low-bit PTQ across diverse tasks.

Peng Xia, Junbiao Pang, Muhammad Ayub Sabir · 0 citations