| Provably robust deep learning via adversarially trained smoothed classifiers H Salman, J Li, I Razenshteyn, P Zhang, H Zhang, S Bubeck, G Yang Advances in Neural Information Processing Systems, 11289-11300, 2019 | 619 | 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 | 485 | 2020 |
| A convex relaxation barrier to tight robustness verification of neural networks H Salman, G Yang, H Zhang, CJ Hsieh, P Zhang Advances in Neural Information Processing Systems, 9832-9842, 2019 | 298 | 2019 |
| Randomized smoothing of all shapes and sizes G Yang, T Duan, JE Hu, H Salman, I Razenshteyn, J Li International Conference on Machine Learning, 10693-10705, 2020 | 234 | 2020 |
| Robustness (python library), 2019 L Engstrom, A Ilyas, H Salman, S Santurkar, D Tsipras URL https://github. com/MadryLab/robustness 4 (4), 4.3, 0 | 234* | |
| Denoised smoothing: A provable defense for pretrained classifiers H Salman, M Sun, G Yang, A Kapoor, JZ Kolter Advances in Neural Information Processing Systems, 2020 | 181 | 2020 |
| Gpt-4o system card A Hurst, A Lerer, AP Goucher, A Perelman, A Ramesh, A Clark, AJ Ostrow, ... arXiv preprint arXiv:2410.21276, 2024 | 132 | 2024 |
| Raising the cost of malicious ai-powered image editing H Salman, A Khaddaj, G Leclerc, A Ilyas, A Madry arXiv preprint arXiv:2302.06588, 2023 | 108 | 2023 |
| A fine-grained spectral perspective on neural networks G Yang, H Salman arXiv preprint arXiv:1907.10599, 2019 | 106 | 2019 |
| FFCV: Accelerating Training by Removing Data Bottlenecks G Leclerc, A Ilyas, L Engstrom, SM Park, H Salman, A Madry | 83* | 2022 |
| Certified patch robustness via smoothed vision transformers H Salman, S Jain, E Wong, A Madry Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 72 | 2022 |
| Ergodic coverage in constrained environments using stochastic trajectory optimization E Ayvali, H Salman, H Choset 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017 | 66 | 2017 |
| 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 | 57 | 2021 |
| 3db: A framework for debugging computer vision models G Leclerc, H Salman, A Ilyas, S Vemprala, L Engstrom, V Vineet, K Xiao, ... Advances in Neural Information Processing Systems 35, 8498-8511, 2022 | 51 | 2022 |
| Learning to sequence robot behaviors for visual navigation H Salman, P Singhal, T Shankar, P Yin, A Salman, W Paivine, G Sartoretti, ... arXiv preprint arXiv:1803.01446, 2018 | 49* | 2018 |
| A data-based perspective on transfer learning S Jain, H Salman, A Khaddaj, E Wong, SM Park, A Mądry Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 42 | 2023 |
| When does bias transfer in transfer learning? H Salman, S Jain, A Ilyas, L Engstrom, E Wong, A Madry arXiv preprint arXiv:2207.02842, 2022 | 37 | 2022 |
| Deep diffeomorphic normalizing flows H Salman, P Yadollahpour, T Fletcher, K Batmanghelich arXiv preprint arXiv:1810.03256, 2018 | 31 | 2018 |
| Missingness bias in model debugging S Jain, H Salman, E Wong, P Zhang, V Vineet, S Vemprala, A Madry arXiv preprint arXiv:2204.08945, 2022 | 30 | 2022 |
| Openai o1 system card A Jaech, A Kalai, A Lerer, A Richardson, A El-Kishky, A Low, A Helyar, ... arXiv preprint arXiv:2412.16720, 2024 | 29 | 2024 |
Aleksander MądryMITVerifierad e-postadress på mit.edu