  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 adversarial classification models
proposed earlier and to maximum-margin classifiers. We also provide a
reformulation of the distributionally robust model for linear classification,
and show it is equivalent to minimizing a regularized ramp loss objective.
Numerical experiments show that, despite the nonconvexity of this formulation,
standard descent methods appear to converge to the global minimizer for this
problem. Inspired by this observation, we show that, for a certain class of
distributions, the only stationary point of the regularized ramp loss
minimization problem is the global minimizer.
