ICML 2019oral21 citations
Monge blunts Bayes: Hardness Results for Adversarial Training
Zac Cranko, Aditya Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walder
Abstract
The last few years have seen a staggering number of empirical studies of the robustness of neural networks in a model of adversarial perturbations of their inputs. Most rely on an adversary which carries out local modifications within prescribed balls. None however has so far questioned the broader picture: how to frame a
BibTeX
@InProceedings{pmlr-v97-cranko19a,
title = {Monge blunts Bayes: Hardness Results for Adversarial Training},
author = {Cranko, Zac and Menon, Aditya and Nock, Richard and Ong, Cheng Soon and Shi, Zhan and Walder, Christian},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {1406--1415},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/cranko19a/cranko19a.pdf},
url = {https://proceedings.mlr.press/v97/cranko19a.html},
abstract = {The last few years have seen a staggering number of empirical studies of the robustness of neural networks in a model of adversarial perturbations of their inputs. Most rely on an adversary which carries out local modifications within prescribed balls. None however has so far questioned the broader picture: how to frame a