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Robustness Guarantees for Adversarially Trained Neural Networks

Poorya Mianjy · Raman Arora

Great Hall & Hall B1+B2 (level 1) #1919
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Tue 12 Dec 3:15 p.m. PST — 5:15 p.m. PST

Abstract: We study robust adversarial training of two-layer neural networks as a bi-level optimization problem. In particular, for the inner loop that implements the adversarial attack during training using projected gradient descent (PGD), we propose maximizing a \emph{lower bound} on the $0/1$-loss by reflecting a surrogate loss about the origin. This allows us to give a convergence guarantee for the inner-loop PGD attack. Furthermore, assuming the data is linearly separable, we provide precise iteration complexity results for end-to-end adversarial training, which holds for any width and initialization. We provide empirical evidence to support our theoretical results.

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