Author

De-Han Wang

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Preprint Sep 2026

Tail-Weight Control and Localized Generalization in Nearly Low-Rank Adversarial Classification

Empirical ramp fitting can assign weight to pure-noise features even when the population optimum ignores them. We quantify this gap for norm-constrained adversarial classification with Gaussian signal and noise. The variance cost relative to normalized signed mean separates into two factors: selecting observations insi...

Kun-Yu Wang, De-Han Wang, Wen-Jun Chen · 0 citations

#artificial intelligence Preprint Sep 2026

Tail-Weight Control and Localized Generalization in Nearly Low-Rank Adversarial Classification

We study norm-constrained linear classification under Eu clidean adversarial perturbations in a Gaussian model with a low-dimen sional informative subspace and an independent noise tail. For bounded ramp loss, we prove that a principal-space witness with risk below one half forces every near-optimal predictor to have s...

Kun-Yu Wang, De-Han Wang, Wen-Jun Chen · 0 citations