Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving the Tightness of Convex Relaxation Bounds for Training
  Certifiably Robust Classifiers   Convex relaxations are effective for training and certifying neural networks
against norm-bounded adversarial attacks, but they leave a large gap between
certifiable and empirical robustness. In principle, convex relaxation can
provide tight bounds if the solution to the relaxed problem is feasible for the
original non-convex problem. We propose two regularizers that can be used to
train neural networks that yield tighter convex relaxation bounds for
robustness. In all of our experiments, the proposed regularizers result in
higher certified accuracy than non-regularized baselines.

20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol