| Synthesizing robust adversarial examples A Athalye, L Engstrom, A Ilyas, K Kwok arXiv preprint arXiv:1707.07397, 2017 | 1160 | 2017 |
| How Does Batch Normalization Help Optimization? S Santurkar, D Tsipras, A Ilyas, A Madry | 1094* | |
| 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 | 980 | 2019 |
| 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 | 722 | 2018 |
| Training GANs with Optimism C Daskalakis, A Ilyas, V Syrgkanis, H Zeng arXiv preprint arXiv:1711.00141, 2017 | 336 | 2017 |
| Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors A Ilyas, L Engstrom, A Madry arXiv preprint arXiv:1807.07978, 2018 | 210 | 2018 |
| 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 | 167* | 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 | 150 | 2017 |
| 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 | 127* | 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 | 123 | 2020 |
| Computer Vision with a Single (Robust) Classifier. S Santurkar, D Tsipras, B Tran, A Ilyas, L Engstrom, A Madry | 121* | 2019 |
| Evaluating and Understanding the Robustness of Adversarial Logit Pairing L Engstrom, A Ilyas, A Athalye arXiv preprint arXiv:1807.10272, 2018 | 108 | 2018 |
| 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 | 98 | 2020 |
| Robustness (python library), 2019 L Engstrom, A Ilyas, S Santurkar, D Tsipras URL https://github. com/MadryLab/robustness, 0 | 87 | |
| 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 | 86* | 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 | 62 | 2020 |
| 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 | 29 | 2020 |
| Estimating Household Transmission of SARS-CoV-2 M Curmei, A Ilyas, O Evans, J Steinhardt medRxiv, 2020 | 25* | 2020 |
| 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 | 21* | 2019 |
| 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 | 20* | 2021 |
Logan EngstromMITVerified email at mit.edu
