| How Does Batch Normalization Help Optimization? S Santurkar, D Tsipras, A Ilyas, A Madry | 2027* | |
| 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 | 1884 | 2019 |
| Synthesizing robust adversarial examples A Athalye, L Engstrom, A Ilyas, K Kwok arXiv preprint arXiv:1707.07397, 2017 | 1797 | 2017 |
| 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 | 1297 | 2018 |
| Training GANs with Optimism C Daskalakis, A Ilyas, V Syrgkanis, H Zeng arXiv preprint arXiv:1711.00141, 2017 | 521 | 2017 |
| 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 | 438* | 2019 |
| Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors A Ilyas, L Engstrom, A Madry arXiv preprint arXiv:1807.07978, 2018 | 401 | 2018 |
| 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 | 399 | 2020 |
| 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 | 323 | 2020 |
| 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 | 243* | 2019 |
| 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 | 213* | 2019 |
| Robustness (python library), 2019 L Engstrom, A Ilyas, S Santurkar, D Tsipras URL https://github. com/MadryLab/robustness, 0 | 201* | |
| 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 | 191 | 2017 |
| 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 | 146 | 2020 |
| Evaluating and Understanding the Robustness of Adversarial Logit Pairing L Engstrom, A Ilyas, A Athalye arXiv preprint arXiv:1807.10272, 2018 | 145 | 2018 |
| 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 | 136* | 2018 |
| Datamodels: Predicting Predictions from Training Data A Ilyas, SM Park, L Engstrom, G Leclerc, A Madry arXiv preprint arXiv:2202.00622, 2022 | 94* | 2022 |
| 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 | 55 | 2020 |
| 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 | 52* | 2021 |
| ffcv G Leclerc, A Ilyas, L Engstrom, SM Park, H Salman, A Madry https://github.com/libffcv/ffcv, 2022 | 46* | 2022 |
Logan EngstromMITVerified email at mit.edu
