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Andrew Ilyas
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Cited by
Year
How Does Batch Normalization Help Optimization?
S Santurkar, D Tsipras, A Ilyas, A Madry
1451*
Synthesizing robust adversarial examples
A Athalye, L Engstrom, A Ilyas, K Kwok
arXiv preprint arXiv:1707.07397, 2017
13612017
Adversarial examples are not bugs, they are features
A Ilyas, S Santurkar, D Tsipras, L Engstrom, B Tran, A Madry
Advances in Neural Information Processing Systems, 125-136, 2019
12972019
Black-box adversarial attacks with limited queries and information
A Ilyas, L Engstrom, A Athalye, J Lin
International Conference on Machine Learning, 2137-2146, 2018
9032018
Training GANs with Optimism
C Daskalakis, A Ilyas, V Syrgkanis, H Zeng
arXiv preprint arXiv:1711.00141, 2017
4152017
Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors
A Ilyas, L Engstrom, A Madry
arXiv preprint arXiv:1807.07978, 2018
2762018
Implementation matters in deep rl: A case study on ppo and trpo
L Engstrom, A Ilyas, S Santurkar, D Tsipras, F Janoos, L Rudolph, ...
International conference on learning representations, 2019
242*2019
Do adversarially robust imagenet models transfer better?
H Salman, A Ilyas, L Engstrom, A Kapoor, A Madry
Advances in Neural Information Processing Systems 33, 2020
2112020
Noise or Signal: The Role of Image Backgrounds in Object Recognition
K Xiao, L Engstrom, A Ilyas, A Madry
arXiv preprint arXiv:2006.09994, 2020
1802020
Learning perceptually-aligned representations via adversarial robustness
L Engstrom, A Ilyas, S Santurkar, D Tsipras, B Tran, A Madry
arXiv preprint arXiv:1906.00945 2 (3), 5, 2019
170*2019
The robust manifold defense: Adversarial training using generative models
A Ilyas, A Jalal, E Asteri, C Daskalakis, AG Dimakis
arXiv preprint arXiv:1712.09196, 2017
1612017
Image synthesis with a single (robust) classifier
S Santurkar, A Ilyas, D Tsipras, L Engstrom, B Tran, A Madry
Advances in Neural Information Processing Systems 32, 2019
153*2019
Robustness (python library), 2019
L Engstrom, A Ilyas, S Santurkar, D Tsipras
URL https://github. com/MadryLab/robustness, 0
132*
Evaluating and Understanding the Robustness of Adversarial Logit Pairing
L Engstrom, A Ilyas, A Athalye
arXiv preprint arXiv:1807.10272, 2018
1202018
A Closer Look at Deep Policy Gradients
A Ilyas, L Engstrom, S Santurkar, D Tsipras, F Janoos, L Rudolph, ...
arXiv preprint arXiv:1811.02553, 2018
106*2018
From imagenet to image classification: Contextualizing progress on benchmarks
D Tsipras, S Santurkar, L Engstrom, A Ilyas, A Madry
International Conference on Machine Learning, 9625-9635, 2020
922020
Identifying statistical bias in dataset replication
L Engstrom, A Ilyas, S Santurkar, D Tsipras, J Steinhardt, A Madry
International Conference on Machine Learning, 2922-2932, 2020
382020
Unadversarial examples: Designing objects for robust vision
H Salman, A Ilyas, L Engstrom, S Vemprala, A Madry, A Kapoor
Thirty-Fifth Conference on Neural Information Processing Systems, 2021
27*2021
A Discussion of'Adversarial Examples Are Not Bugs, They Are Features': Discussion and Author Responses
L Engstrom, A Ilyas, A Madry, S Santurkar, B Tran, D Tsipras
Distill 4 (8), e00019. 7, 2019
25*2019
Datamodels: Predicting Predictions from Training Data
A Ilyas, SM Park, L Engstrom, G Leclerc, A Madry
arXiv preprint arXiv:2202.00622, 2022
24*2022
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