Certifying Some Distributional Robustness with Principled Adversarial
Training

Certifying Some Distributional Robustness with Principled Adversarial Training

29 October 2017

Hongseok Namkoong

Papers citing "Certifying Some Distributional Robustness with Principled Adversarial Training"

15 / 165 papers shown

Title
Certified Adversarial Robustness with Additive Noise Bai Li Changyou Chen Wenlin Wang Lawrence Carin AAML 17 341 0 10 Sep 2018
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability Kai Y. Xiao Vincent Tjeng Nur Muhammad (Mahi) Shafiullah A. Madry AAML OOD 9 199 0 09 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 33 226 0 18 Jul 2018
Generalizing to Unseen Domains via Adversarial Data Augmentation Riccardo Volpi Hongseok Namkoong Ozan Sener John C. Duchi Vittorio Murino Silvio Savarese OOD 29 768 0 30 May 2018
Local Saddle Point Optimization: A Curvature Exploitation Approach Leonard Adolphs Hadi Daneshmand Aurélien Lucchi Thomas Hofmann 15 107 0 15 May 2018
Semantic Adversarial Deep Learning S. Seshia S. Jha T. Dreossi AAML SILM 13 90 0 19 Apr 2018
Adversarial Defense based on Structure-to-Signal Autoencoders Joachim Folz Sebastián M. Palacio Jörn Hees Damian Borth Andreas Dengel AAML 20 31 0 21 Mar 2018
Adversarial Logit Pairing Harini Kannan Alexey Kurakin Ian Goodfellow AAML 8 624 0 16 Mar 2018
Robust GANs against Dishonest Adversaries Zhi Xu Chengtao Li Stefanie Jegelka AAML 32 3 0 27 Feb 2018
The Robust Manifold Defense: Adversarial Training using Generative Models A. Jalal Andrew Ilyas C. Daskalakis A. Dimakis AAML 23 174 0 26 Dec 2017
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples Yizhen Wang S. Jha Kamalika Chaudhuri AAML 11 154 0 13 Jun 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 226 1,835 0 03 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016
Safety Verification of Deep Neural Networks Xiaowei Huang M. Kwiatkowska Sen Wang Min Wu AAML 178 932 0 21 Oct 2016
Sample Out-Of-Sample Inference Based on Wasserstein Distance Jose H. Blanchet Yang Kang 23 35 0 04 May 2016