| 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 32, 2019 | 2037 | 2019 |
| Robustness may be at odds with accuracy D Tsipras, S Santurkar, L Engstrom, A Turner, A Madry arXiv preprint arXiv:1805.12152, 2018 | 1966 | 2018 |
| Synthesizing robust adversarial examples A Athalye, L Engstrom, A Ilyas, K Kwok arXiv preprint arXiv:1707.07397, 2017 | 1944 | 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 | 1406 | 2018 |
| A rotation and a translation suffice: Fooling cnns with simple transformations L Engstrom, B Tran, D Tsipras, L Schmidt, A Madry | 884* | 2017 |
| Do adversarially robust imagenet models transfer better? H Salman, A Ilyas, L Engstrom, A Kapoor, A Madry Advances in Neural Information Processing Systems 33, 3533-3545, 2020 | 437 | 2020 |
| Prior convictions: Black-box adversarial attacks with bandits and priors A Ilyas, L Engstrom, A Madry arXiv preprint arXiv:1807.07978, 2018 | 432 | 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 | 376 | 2020 |
| 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 | 285 | 2019 |
| Adversarial robustness as a prior for learned representations L Engstrom, A Ilyas, S Santurkar, D Tsipras, B Tran, A Madry arXiv preprint arXiv:1906.00945, 2019 | 261* | 2019 |
| Implementation matters in deep policy gradients: A case study on ppo and trpo L Engstrom, A Ilyas, S Santurkar, D Tsipras, F Janoos, L Rudolph, ... arXiv preprint arXiv:2005.12729, 2020 | 234 | 2020 |
| Computer vision with a single (robust) classifier S Santurkar, D Tsipras, B Tran, A Ilyas, L Engstrom, A Madry arXiv preprint arXiv:1906.09453 4, 1, 2019 | 230* | 2019 |
| Robustness (python library), 2019 L Engstrom, A Ilyas, H Salman, S Santurkar, D Tsipras URL https://github. com/MadryLab/robustness 4 (4), 4.3, 2019 | 219 | 2019 |
| 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 | 159 | 2020 |
| Evaluating and understanding the robustness of adversarial logit pairing L Engstrom, A Ilyas, A Athalye arXiv preprint arXiv:1807.10272, 2018 | 147 | 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 | 142* | 2018 |
| Datamodels: Predicting predictions from training data A Ilyas, SM Park, L Engstrom, G Leclerc, A Madry arXiv preprint arXiv:2202.00622, 2022 | 103 | 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 | 58 | 2020 |
| FFCV: Accelerating training by removing data bottlenecks G Leclerc, A Ilyas, L Engstrom, SM Park, H Salman, A Mądry Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 53 | 2023 |
| Unadversarial examples: Designing objects for robust vision H Salman, A Ilyas, L Engstrom, S Vemprala, A Madry, A Kapoor Advances in Neural Information Processing Systems 34, 15270-15284, 2021 | 51 | 2021 |
Andrew IlyasMassachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
