@inproceedings{fab3146bdd2443cdbb183f83c74f66d9,
title = "Robust Machine Learning via Privacy/ Rate-Distortion Theory",
abstract = "Robust machine learning formulations have emerged to address the prevalent vulnerability of deep neural networks to adversarial examples. Our work draws the connection between optimal robust learning and the privacy-utility tradeoff problem, which is a generalization of the rate-distortion problem. The saddle point of the game between a robust classifier and an adversarial perturbation can be found via the solution of a maximum conditional entropy problem. This information-theoretic perspective sheds light on the fundamental tradeoff between robustness and clean data performance, which ultimately arises from the geometric structure of the underlying data distribution and perturbation constraints.",
keywords = "adversarial examples, privacy, robust learning",
author = "Ye Wang and Shuchin Aeron and Rakin, {Adnan Siraj} and Toshiaki Koike-Akino and Pierre Moulin",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Symposium on Information Theory, ISIT 2021 ; Conference date: 12-07-2021 Through 20-07-2021",
year = "2021",
month = jul,
day = "12",
doi = "10.1109/ISIT45174.2021.9517751",
language = "English (US)",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1320--1325",
booktitle = "2021 IEEE International Symposium on Information Theory, ISIT 2021 - Proceedings",
address = "United States",
}