Feb. 9, 2024, 5:43 a.m. | Kasimir Tanner Matteo Vilucchio Bruno Loureiro Florent Krzakala

cs.LG updates on arXiv.org arxiv.org

This work investigates adversarial training in the context of margin-based linear classifiers in the high-dimensional regime where the dimension $d$ and the number of data points $n$ diverge with a fixed ratio $\alpha = n / d$. We introduce a tractable mathematical model where the interplay between the data and adversarial attacker geometries can be studied, while capturing the core phenomenology observed in the adversarial robustness literature. Our main theoretical contribution is an exact asymptotic description of the sufficient statistics …

adversarial adversarial training alpha classifiers cond-mat.dis-nn context cs.lg data geometry linear stat.ml tractable trade training work

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