Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Convergence of Certified Robust Training with Interval Bound
  Propagation   Interval Bound Propagation (IBP) is so far the base of state-of-the-art
methods for training neural networks with certifiable robustness guarantees
when potential adversarial perturbations present, while the convergence of IBP
training remains unknown in existing literature. In this paper, we present a
theoretical analysis on the convergence of IBP training. With an
overparameterized assumption, we analyze the convergence of IBP robust
training. We show that when using IBP training to train a randomly initialized
two-layer ReLU neural network with logistic loss, gradient descent can linearly
converge to zero robust training error with a high probability if we have
sufficiently small perturbation radius and large network width.

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The categories of this paper are: math
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
29
The categories of this paper are: funct-an