Adversarial Rademacher Complexity of Deep Neural Networks

27 November 2022

Jiancong Xiao

Papers citing "Adversarial Rademacher Complexity of Deep Neural Networks"

20 / 20 papers shown

Title
Fixing Data Augmentation to Improve Adversarial Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 87 272 0 02 Mar 2021
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 52 331 0 07 Oct 2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning Zeyuan Allen-Zhu Yuanzhi Li MLT AAML 74 150 0 20 May 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 277 834 0 19 Feb 2020
Adversarial Training Can Hurt Generalization Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang 82 243 0 14 Jun 2019
VC Classes are Adversarially Robustly Learnable, but Only Improperly Omar Montasser Steve Hanneke Nathan Srebro 35 139 0 12 Feb 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 152 2,039 0 08 Feb 2019
On the Geometry of Adversarial Examples Marc Khoury Dylan Hadfield-Menell AAML 59 79 0 01 Nov 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 137 790 0 30 Apr 2018
Certified Robustness to Adversarial Examples with Differential Privacy Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel J. Hsu Suman Jana SILM AAML 96 934 0 09 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 224 3,186 0 01 Feb 2018
Size-Independent Sample Complexity of Neural Networks Noah Golowich Alexander Rakhlin Ohad Shamir 154 547 0 18 Dec 2017
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models Pin-Yu Chen Huan Zhang Yash Sharma Jinfeng Yi Cho-Jui Hsieh AAML 80 1,879 0 14 Aug 2017
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks Behnam Neyshabur Srinadh Bhojanapalli Nathan Srebro 83 607 0 29 Jul 2017
Spectrally-normalized margin bounds for neural networks Peter L. Bartlett Dylan J. Foster Matus Telgarsky ODL 202 1,220 0 26 Jun 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 307 12,069 0 19 Jun 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 266 8,555 0 16 Aug 2016
Norm-Based Capacity Control in Neural Networks Behnam Neyshabur Ryota Tomioka Nathan Srebro 290 588 0 27 Feb 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 277 19,066 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 270 14,927 1 21 Dec 2013