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arXiv Machine Learning

On the tradeoff between robustness and geometric accuracy in adversarial training

arXiv:2610. 22955v1 Announce Type: new Abstract: For non-parametric binary classification in the presence of an adversary, we prove an optimal quantitative estimate for adversarial classifiers controlling geometric accuracy, as measured by the Hausdorff distance to the Bayes classifier, in terms or their robustness, as encoded by the adversarial budget $\varepsilon$.

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