Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness

29 May 2019

Papers citing "Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness"

28 / 28 papers shown

Title
Lower Bounds on Adversarial Robustness from Optimal Transport A. Bhagoji Daniel Cullina Prateek Mittal OOD OT AAML 58 93 0 26 Sep 2019
Towards Stable and Efficient Training of Verifiably Robust Neural Networks Huan Zhang Hongge Chen Chaowei Xiao Sven Gowal Robert Stanforth Yue Liu Duane S. Boning Cho-Jui Hsieh AAML 67 347 0 14 Jun 2019
Adversarial Training Can Hurt Generalization Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang 82 242 0 14 Jun 2019
VC Classes are Adversarially Robustly Learnable, but Only Improperly Omar Montasser Steve Hanneke Nathan Srebro 29 139 0 12 Feb 2019
Understanding the (un)interpretability of natural image distributions using generative models Ryen Krusinga Sohil Shah Matthias Zwicker Tom Goldstein David Jacobs DiffM FAtt GAN 56 11 0 06 Jan 2019
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models Sven Gowal Krishnamurthy Dvijotham Robert Stanforth Rudy Bunel Chongli Qin J. Uesato Relja Arandjelović Timothy A. Mann Pushmeet Kohli AAML 79 556 0 30 Oct 2018
Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution Dimitrios I. Diochnos Saeed Mahloujifar Mohammad Mahmoody AAML 41 72 0 29 Oct 2018
Rademacher Complexity for Adversarially Robust Generalization Dong Yin Kannan Ramchandran Peter L. Bartlett AAML 89 260 0 29 Oct 2018
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure Saeed Mahloujifar Dimitrios I. Diochnos Mohammad Mahmoody 54 151 0 09 Sep 2018
Are adversarial examples inevitable? Ali Shafahi Wenjie Huang Christoph Studer Soheil Feizi Tom Goldstein SILM 62 282 0 06 Sep 2018
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 73 228 0 18 Jul 2018
Scaling provable adversarial defenses Eric Wong Frank R. Schmidt J. H. Metzen J. Zico Kolter AAML 76 448 0 31 May 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 102 1,778 0 30 May 2018
Adversarial examples from computational constraints Sébastien Bubeck Eric Price Ilya P. Razenshteyn AAML 108 231 0 25 May 2018
Towards the first adversarially robust neural network model on MNIST Lukas Schott Jonas Rauber Matthias Bethge Wieland Brendel AAML OOD 60 370 0 23 May 2018
Adversarial vulnerability for any classifier Alhussein Fawzi Hamza Fawzi Omar Fawzi AAML 73 250 0 23 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 219 3,185 0 01 Feb 2018
Certified Defenses against Adversarial Examples Aditi Raghunathan Jacob Steinhardt Percy Liang AAML 105 968 0 29 Jan 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 125 1,409 0 08 Dec 2017
Provable defenses against adversarial examples via the convex outer adversarial polytope Eric Wong J. Zico Kolter AAML 123 1,501 0 02 Nov 2017
Certifying Some Distributional Robustness with Principled Adversarial Training Aman Sinha Hongseok Namkoong Riccardo Volpi John C. Duchi OOD 125 863 0 29 Oct 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 280 8,878 0 25 Aug 2017
Exploring Generalization in Deep Learning Behnam Neyshabur Srinadh Bhojanapalli David A. McAllester Nathan Srebro FAtt 148 1,255 0 27 Jun 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 304 12,063 0 19 Jun 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 261 8,552 0 16 Aug 2016
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 92 3,072 0 14 Nov 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 277 19,049 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 14,918 1 21 Dec 2013