The paper introduces probabilistic adversarial training, a method that enhances robustness by reducing the overlap between a distance-based distribution and a victim-classifier-induced distribution. It derives a KL-based lower bound on probabilistic robustness, which serves as a tractable surrogate objective. Experiments confirm that this approach consistently improves probabilistic robustness and can even boost non-probabilistic adversarial training methods through an induced scaling factor.
By Andi Zhang, Xingyu Zhao, Siddartha Khastgir
arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.
By Nicolas Sournac, Ahmed Baha Ben Jmaa, Bertrand Braeckeveldt
arXiv:2506.12454v2 Announce Type: replace-cross
Abstract: What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this wor...
By Matteo Vilucchio, Lenka Zdeborov\'a, Bruno Loureiro
arXiv:2510. 09288v2 Announce Type: replace-cross Abstract: The vulnerability of machine learning models to adversarial attacks remains a critical societal security challenge.
By Pablo G. Arce, Roi Naveiro, David R\'ios Insua
We study the problem of \emph{adversarially robust} PAC learning. In this framework, the learner observes independent samples from an unknown distribution over $\mathcal{X} \times \{0,1\}$, as in clas...
arXiv:2610.22564v1 Announce Type: new
Abstract: Data-driven approaches often face ambiguity in datasets due to sensor or target noise, insufficient information, and noise in the input data. While bes...
By Nina Maria Gottschling, Jakob Gawlikowski, Michele Caprio