Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Classification via Distributional Robustness with
  Wasserstein Ambiguity   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.

7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th