Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Distributionally Robust Area Under Curve Maximization Model   Area under ROC curve (AUC) is a widely used performance measure for
classification models. We propose two new distributionally robust AUC
maximization models (DR-AUC) that rely on the Kantorovich metric and
approximate the AUC with the hinge loss function. We consider the two cases
with respectively fixed and variable support for the worst-case distribution.
We use duality theory to reformulate the DR-AUC models and derive tractable
convex optimization problems. The numerical experiments show that the proposed
DR-AUC models -- benchmarked with the standard deterministic AUC and the
support vector machine models - perform better in general and in particular
improve the worst-case out-of-sample performance over the majority of the
considered datasets, thereby showing their robustness. The results are
particularly encouraging since our numerical experiments are conducted with
training sets of small size which have been known to be conducive to low
out-of-sample performance.

28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex