Theoretically Principled Trade-off between Robustness and Accuracy

24 January 2019

Papers citing "Theoretically Principled Trade-off between Robustness and Accuracy"

50 / 591 papers shown

Title
Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman Andrew Ilyas Logan Engstrom Ashish Kapoor A. Madry 37 417 0 16 Jul 2020
On Adversarial Robustness: A Neural Architecture Search perspective Chaitanya Devaguptapu Devansh Agarwal Gaurav Mittal Pulkit Gopalani V. Balasubramanian OOD AAML 12 33 0 16 Jul 2020
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training Kai Liang Tan Yasaman Esfandiari Xian Yeow Lee Aakanksha S. Sarkar AAML 26 55 0 14 Jul 2020
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning Feng Wang Chen Zhong M. C. Gursoy Senem Velipasalar AAML 18 8 0 12 Jul 2020
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples S. Goldwasser Adam Tauman Kalai Y. Kalai Omar Montasser AAML 19 38 0 10 Jul 2020
Boundary thickness and robustness in learning models Yaoqing Yang Rekha Khanna Yaodong Yu A. Gholami Kurt Keutzer Joseph E. Gonzalez Kannan Ramchandran Michael W. Mahoney OOD 18 37 0 09 Jul 2020
How benign is benign overfitting? Amartya Sanyal P. Dokania Varun Kanade Philip Torr NoLa AAML 23 57 0 08 Jul 2020
Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori Achal Dave Vaishaal Shankar Nicholas Carlini Benjamin Recht Ludwig Schmidt OOD 48 536 0 01 Jul 2020
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification Chen Dan Yuting Wei Pradeep Ravikumar 26 45 0 29 Jun 2020
Proper Network Interpretability Helps Adversarial Robustness in Classification Akhilan Boopathy Sijia Liu Gaoyuan Zhang Cynthia Liu Pin-Yu Chen Shiyu Chang Luca Daniel AAML FAtt 27 66 0 26 Jun 2020
Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances Meisam Razaviyayn Tianjian Huang Songtao Lu Maher Nouiehed Maziar Sanjabi Mingyi Hong 19 108 0 15 Jun 2020
Adversarial Self-Supervised Contrastive Learning Minseon Kim Jihoon Tack Sung Ju Hwang SSL 28 247 0 13 Jun 2020
Large-Scale Adversarial Training for Vision-and-Language Representation Learning Zhe Gan Yen-Chun Chen Linjie Li Chen Zhu Yu Cheng Jingjing Liu ObjD VLM 35 488 0 11 Jun 2020
Probably Approximately Correct Constrained Learning Luiz F. O. Chamon Alejandro Ribeiro 22 38 0 09 Jun 2020
Provable tradeoffs in adversarially robust classification Yan Sun Hamed Hassani David Hong Alexander Robey 23 53 0 09 Jun 2020
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization Daniel Golovin Qiuyi Zhang 24 70 0 08 Jun 2020
Principled learning method for Wasserstein distributionally robust optimization with local perturbations Yongchan Kwon Wonyoung Hedge Kim Joong-Ho Won M. Paik 22 12 0 05 Jun 2020
Adversarial Classification via Distributional Robustness with Wasserstein Ambiguity Nam Ho-Nguyen Stephen J. Wright OOD 50 16 0 28 May 2020
Calibrated Surrogate Losses for Adversarially Robust Classification Han Bao Clayton Scott Masashi Sugiyama 29 45 0 28 May 2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning Zeyuan Allen-Zhu Yuanzhi Li MLT AAML 39 147 0 20 May 2020
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks Linhai Ma Liang Liang AAML 26 18 0 19 May 2020
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition Tianhang Zheng Sheng Liu Changyou Chen Junsong Yuan Baochun Li K. Ren AAML 21 17 0 14 May 2020
DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses Yaxin Li Wei Jin Han Xu Jiliang Tang AAML 32 131 0 13 May 2020
Class-Aware Domain Adaptation for Improving Adversarial Robustness Xianxu Hou Jingxin Liu Bolei Xu Xiaolong Wang Bozhi Liu Guoping Qiu OOD AAML 43 8 0 10 May 2020
Adversarial Training against Location-Optimized Adversarial Patches Sukrut Rao David Stutz Bernt Schiele AAML 19 91 0 05 May 2020
Robust Encodings: A Framework for Combating Adversarial Typos Erik Jones Robin Jia Aditi Raghunathan Percy Liang AAML 142 102 0 04 May 2020
Towards Feature Space Adversarial Attack Qiuling Xu Guanhong Tao Shuyang Cheng Xinming Zhang GAN AAML 25 25 0 26 Apr 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 31 8 0 23 Apr 2020
Single-step Adversarial training with Dropout Scheduling S. VivekB. R. Venkatesh Babu OOD AAML 18 71 0 18 Apr 2020
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning Tianlong Chen Sijia Liu Shiyu Chang Yu Cheng Lisa Amini Zhangyang Wang AAML 18 246 0 28 Mar 2020
DP-Net: Dynamic Programming Guided Deep Neural Network Compression Dingcheng Yang Wenjian Yu Ao Zhou Haoyuan Mu G. Yao Xiaoyi Wang 21 6 0 21 Mar 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 75 99 0 20 Mar 2020
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations Huan Zhang Hongge Chen Chaowei Xiao Bo-wen Li Mingyan D. Liu Duane S. Boning Cho-Jui Hsieh AAML 44 261 0 19 Mar 2020
Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates Amin Ghiasi Ali Shafahi Tom Goldstein 33 55 0 19 Mar 2020
Toward Adversarial Robustness via Semi-supervised Robust Training Yiming Li Baoyuan Wu Yan Feng Yanbo Fan Yong Jiang Zhifeng Li Shutao Xia AAML 87 13 0 16 Mar 2020
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks Y. Tashiro Yang Song Stefano Ermon AAML 14 13 0 15 Mar 2020
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Saehyung Lee Hyungyu Lee Sungroh Yoon AAML 163 113 0 05 Mar 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 72 63 0 02 Mar 2020
On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks Yue Zhao Yuwei Wu Caihua Chen A. Lim 3DPC 16 70 0 27 Feb 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 47 787 0 26 Feb 2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger Jingfeng Zhang Xilie Xu Bo Han Gang Niu Li-zhen Cui Masashi Sugiyama Mohan S. Kankanhalli AAML 33 397 0 26 Feb 2020
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization Yifei Min Lin Chen Amin Karbasi AAML 37 69 0 25 Feb 2020
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang AAML 51 223 0 25 Feb 2020
MaxUp: A Simple Way to Improve Generalization of Neural Network Training Chengyue Gong Tongzheng Ren Mao Ye Qiang Liu AAML 27 56 0 20 Feb 2020
CAT: Customized Adversarial Training for Improved Robustness Minhao Cheng Qi Lei Pin-Yu Chen Inderjit Dhillon Cho-Jui Hsieh OOD AAML 35 114 0 17 Feb 2020
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models Lin Chen Yifei Min Mingrui Zhang Amin Karbasi OOD 38 64 0 11 Feb 2020
Robustness of Bayesian Neural Networks to Gradient-Based Attacks Ginevra Carbone Matthew Wicker Luca Laurenti A. Patané Luca Bortolussi G. Sanguinetti AAML 38 77 0 11 Feb 2020
$Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images$ Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images Avrim Blum Travis Dick N. Manoj Hongyang R. Zhang AAML 31 79 0 10 Feb 2020
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius Runtian Zhai Chen Dan Di He Huan Zhang Boqing Gong Pradeep Ravikumar Cho-Jui Hsieh Liwei Wang OOD AAML 21 177 0 08 Jan 2020
Efficient Adversarial Training with Transferable Adversarial Examples Haizhong Zheng Ziqi Zhang Juncheng Gu Honglak Lee A. Prakash AAML 24 108 0 27 Dec 2019