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Title: Robust Learning for Data Poisoning Attacks
We investigate the robustness of stochastic approximation approaches against data poisoning attacks. We focus on two-layer neural networks with ReLU activation and show that under a specific notion of separability in the RKHS induced by the infinite-width network, training (finite-width) networks with stochastic gradient descent is robust against data poisoning attacks. Interestingly, we find that in addition to a lower bound on the width of the network, which is standard in the literature, we also require a distribution-dependent upper bound on the width for robust generalization. We provide extensive empirical evaluations that support and validate our theoretical results.  more » « less
Award ID(s):
1838139 1943251 1546482
PAR ID:
10312903
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Volume:
139
ISSN:
2640-3498
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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