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1906.04584
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Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Neural Information Processing Systems (NeurIPS), 2019
9 June 2019
Hadi Salman
Greg Yang
Jungshian Li
Pengchuan Zhang
Huan Zhang
Ilya P. Razenshteyn
Sébastien Bubeck
AAML
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Papers citing
"Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"
50 / 390 papers shown
Understanding Catastrophic Overfitting in Single-step Adversarial Training
AAAI Conference on Artificial Intelligence (AAAI), 2020
Hoki Kim
Woojin Lee
Jaewook Lee
AAML
394
124
0
05 Oct 2020
Do Wider Neural Networks Really Help Adversarial Robustness?
Neural Information Processing Systems (NeurIPS), 2020
Boxi Wu
Jinghui Chen
Deng Cai
Xiaofei He
Quanquan Gu
AAML
406
104
0
03 Oct 2020
Efficient Robust Training via Backward Smoothing
AAAI Conference on Artificial Intelligence (AAAI), 2020
Jinghui Chen
Yu Cheng
Zhe Gan
Quanquan Gu
Jingjing Liu
AAML
192
44
0
03 Oct 2020
Query complexity of adversarial attacks
International Conference on Machine Learning (ICML), 2020
Grzegorz Gluch
R. Urbanke
AAML
209
7
0
02 Oct 2020
Block-wise Image Transformation with Secret Key for Adversarially Robust Defense
IEEE Transactions on Information Forensics and Security (IEEE TIFS), 2020
Maungmaung Aprilpyone
Hitoshi Kiya
144
58
0
02 Oct 2020
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time
Neural Information Processing Systems (NeurIPS), 2020
Ferran Alet
Maria Bauza
Kenji Kawaguchi
Nurullah Giray Kuru
Tomas Lozano-Perez
L. Kaelbling
AI4CE
290
16
0
22 Sep 2020
Efficient Certification of Spatial Robustness
AAAI Conference on Artificial Intelligence (AAAI), 2020
Anian Ruoss
Maximilian Baader
Mislav Balunović
Martin Vechev
AAML
139
26
0
19 Sep 2020
Certifying Confidence via Randomized Smoothing
Neural Information Processing Systems (NeurIPS), 2020
Aounon Kumar
Alexander Levine
Soheil Feizi
Tom Goldstein
UQCV
241
41
0
17 Sep 2020
Risk Bounds for Robust Deep Learning
Johannes Lederer
OOD
146
16
0
14 Sep 2020
SoK: Certified Robustness for Deep Neural Networks
IEEE Symposium on Security and Privacy (IEEE S&P), 2020
Linyi Li
Tao Xie
Yue Liu
AAML
757
143
0
09 Sep 2020
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
International Conference on Machine Learning (ICML), 2020
Aleksandar Bojchevski
Johannes Klicpera
Stephan Günnemann
AAML
288
93
0
29 Aug 2020
Adversarially Robust Learning via Entropic Regularization
Frontiers in Artificial Intelligence (FAI), 2020
Gauri Jagatap
Ameya Joshi
A. B. Chowdhury
S. Garg
Chinmay Hegde
OOD
248
12
0
27 Aug 2020
On the Generalization Properties of Adversarial Training
Yue Xing
Qifan Song
Guang Cheng
AAML
233
36
0
15 Aug 2020
Provably Robust Adversarial Examples
International Conference on Learning Representations (ICLR), 2020
Dimitar I. Dimitrov
Gagandeep Singh
Timon Gehr
Martin Vechev
AAML
214
12
0
23 Jul 2020
Do Adversarially Robust ImageNet Models Transfer Better?
Neural Information Processing Systems (NeurIPS), 2020
Hadi Salman
Andrew Ilyas
Logan Engstrom
Ashish Kapoor
Aleksander Madry
333
467
0
16 Jul 2020
Adversarial Examples and Metrics
Nico Döttling
Kathrin Grosse
Michael Backes
Ian Molloy
AAML
118
0
0
14 Jul 2020
Adversarial robustness via robust low rank representations
Neural Information Processing Systems (NeurIPS), 2020
Pranjal Awasthi
Himanshu Jain
A. S. Rawat
Aravindan Vijayaraghavan
AAML
191
25
0
13 Jul 2020
Measuring Robustness to Natural Distribution Shifts in Image Classification
Rohan Taori
Achal Dave
Vaishaal Shankar
Nicholas Carlini
Benjamin Recht
Ludwig Schmidt
OOD
557
629
0
01 Jul 2020
Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications
Eric Wong
Tim Schneider
Joerg Schmitt
Frank R. Schmidt
J. Zico Kolter
AAML
203
12
0
30 Jun 2020
Black-box Certification and Learning under Adversarial Perturbations
H. Ashtiani
Vinayak Pathak
Ruth Urner
AAML
188
20
0
30 Jun 2020
Deep Partition Aggregation: Provable Defense against General Poisoning Attacks
Alexander Levine
Soheil Feizi
AAML
193
162
0
26 Jun 2020
Counterexample-Guided Learning of Monotonic Neural Networks
Aishwarya Sivaraman
G. Farnadi
T. Millstein
Karen Ullrich
180
62
0
16 Jun 2020
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
Chen Liu
Mathieu Salzmann
Tao Lin
Ryota Tomioka
Sabine Süsstrunk
AAML
331
92
0
15 Jun 2020
Adversarial Self-Supervised Contrastive Learning
Neural Information Processing Systems (NeurIPS), 2020
Minseon Kim
Jihoon Tack
Sung Ju Hwang
SSL
244
275
0
13 Jun 2020
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples
Neural Information Processing Systems (NeurIPS), 2020
Richard Y. Zhang
AAML
237
27
0
11 Jun 2020
Backdoor Smoothing: Demystifying Backdoor Attacks on Deep Neural Networks
Computers & security (CS), 2020
Kathrin Grosse
Taesung Lee
Battista Biggio
Youngja Park
Michael Backes
Ian Molloy
AAML
169
13
0
11 Jun 2020
Deterministic Gaussian Averaged Neural Networks
Ryan Campbell
Chris Finlay
Adam M. Oberman
FedML
102
1
0
10 Jun 2020
Provable tradeoffs in adversarially robust classification
Guang Cheng
Hamed Hassani
David Hong
Avi Schwarzschild
504
58
0
09 Jun 2020
Towards an Intrinsic Definition of Robustness for a Classifier
Théo Giraudon
Vincent Gripon
Matthias Löwe
Franck Vermet
OOD
AAML
98
2
0
09 Jun 2020
Consistency Regularization for Certified Robustness of Smoothed Classifiers
Jongheon Jeong
Jinwoo Shin
AAML
297
96
0
07 Jun 2020
Second-Order Provable Defenses against Adversarial Attacks
International Conference on Machine Learning (ICML), 2020
Sahil Singla
Soheil Feizi
AAML
177
63
0
01 Jun 2020
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions
Annual Meeting of the Association for Computational Linguistics (ACL), 2020
Mao Ye
Chengyue Gong
Qiang Liu
AAML
192
112
0
29 May 2020
Calibrated Surrogate Losses for Adversarially Robust Classification
Annual Conference Computational Learning Theory (COLT), 2020
Han Bao
Clayton Scott
Masashi Sugiyama
228
47
0
28 May 2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning
Zeyuan Allen-Zhu
Yuanzhi Li
MLT
AAML
379
167
0
20 May 2020
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data
Avi Schwarzschild
Hamed Hassani
George J. Pappas
OOD
295
42
0
20 May 2020
Enhancing Certified Robustness via Smoothed Weighted Ensembling
Chizhou Liu
Yunzhen Feng
Ranran Wang
Bin Dong
AAML
222
12
0
19 May 2020
PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking
Chong Xiang
A. Bhagoji
Vikash Sehwag
Prateek Mittal
AAML
314
29
0
17 May 2020
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness
Tianhang Zheng
Haiyan Zhao
Baochun Li
Jinhui Xu
AAML
119
0
0
15 May 2020
Provable Robust Classification via Learned Smoothed Densities
Saeed Saremi
R. Srivastava
AAML
171
3
0
09 May 2020
Improved Image Wasserstein Attacks and Defenses
J. E. Hu
Adith Swaminathan
Hadi Salman
Greg Yang
AAML
OOD
178
11
0
26 Apr 2020
Towards Deep Learning Models Resistant to Large Perturbations
Amirreza Shaeiri
Rozhin Nobahari
M. Rohban
OOD
AAML
191
14
0
30 Mar 2020
Safety-Aware Hardening of 3D Object Detection Neural Network Systems
International Conference on Computer Safety, Reliability, and Security (SAFECOMP), 2020
Chih-Hong Cheng
3DPC
247
12
0
25 Mar 2020
Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates
International Conference on Learning Representations (ICLR), 2020
Amin Ghiasi
Ali Shafahi
Tom Goldstein
153
56
0
19 Mar 2020
Vulnerabilities of Connectionist AI Applications: Evaluation and Defence
Frontiers in Big Data (Front. Big Data), 2020
Christian Berghoff
Matthias Neu
Arndt von Twickel
AAML
206
26
0
18 Mar 2020
When are Non-Parametric Methods Robust?
International Conference on Machine Learning (ICML), 2020
Robi Bhattacharjee
Kamalika Chaudhuri
AAML
275
27
0
13 Mar 2020
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
International Conference on Machine Learning (ICML), 2020
Raphaël Dang-Nhu
Gagandeep Singh
Pavol Bielik
Martin Vechev
AI4TS
AAML
178
25
0
08 Mar 2020
A Closer Look at Accuracy vs. Robustness
Yao-Yuan Yang
Cyrus Rashtchian
Hongyang R. Zhang
Ruslan Salakhutdinov
Kamalika Chaudhuri
OOD
357
31
0
05 Mar 2020
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Hadi Salman
Mingjie Sun
Greg Yang
Ashish Kapoor
J. Zico Kolter
225
23
0
04 Mar 2020
Analyzing Accuracy Loss in Randomized Smoothing Defenses
Yue Gao
Harrison Rosenberg
Kassem Fawaz
S. Jha
Justin Hsu
AAML
163
6
0
03 Mar 2020
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Neural Information Processing Systems (NeurIPS), 2020
Zhuolin Yang
Zhikuan Zhao
Wei Ping
Jiawei Zhang
Linyi Li
...
Bojan Karlas
Ji Liu
Heng Guo
Ce Zhang
Yue Liu
AAML
635
15
0
28 Feb 2020
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