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Adversarial Classification via Distributional Robustness with Wasserstein Ambiguity

28 May 2020 • Nam Ho-Nguyen • Stephen J. Wright

We study a model for adversarial classification based on distributionally robust chance constraints. We show that under Wasserstein ambiguity, the model aims to minimize the conditional value-at-risk of the distance to misclassification, and we explore links to previous adversarial classification models and maximum margin classifiers... (read more)

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Adversarial Classification via Distributional Robustness with Wasserstein Ambiguity

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