| Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 8907 | 2023 |
| How does batch normalization help optimization? S Santurkar, D Tsipras, A Ilyas, A Madry Advances in neural information processing systems 31, 2018 | 2335 | 2018 |
| 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 | 2213 | 2019 |
| Robustness may be at odds with accuracy D Tsipras, S Santurkar, L Engstrom, A Turner, A Madry arXiv preprint arXiv:1805.12152, 2018 | 2122 | 2018 |
| Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022 | 1225 | 2022 |
| Adversarially robust generalization requires more data L Schmidt, S Santurkar, D Tsipras, K Talwar, A Madry Advances in neural information processing systems 31, 2018 | 933 | 2018 |
| 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 | 452 | 2020 |
| Whose opinions do language models reflect? S Santurkar, E Durmus, F Ladhak, C Lee, P Liang, T Hashimoto International Conference on Machine Learning, 29971-30004, 2023 | 401 | 2023 |
| 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 | 274* | 2019 |
| Generative compression S Santurkar, D Budden, N Shavit 2018 Picture Coding Symposium (PCS), 258-262, 2018 | 258 | 2018 |
| 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 | 241 | 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 | 240* | 2019 |
| Breeds: Benchmarks for subpopulation shift S Santurkar, D Tsipras, A Madry arXiv preprint arXiv:2008.04859, 2020 | 197 | 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 | 196 | 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 | 173 | 2020 |
| Data selection for language models via importance resampling SM Xie, S Santurkar, T Ma, PS Liang Advances in Neural Information Processing Systems 36, 34201-34227, 2023 | 161 | 2023 |
| 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 | 161* | 2018 |
| A classification-based study of covariate shift in gan distributions S Santurkar, L Schmidt, A Madry International Conference on Machine Learning, 4480-4489, 2018 | 95* | 2018 |
| Editing a classifier by rewriting its prediction rules S Santurkar, D Tsipras, M Elango, D Bau, A Torralba, A Madry Advances in Neural Information Processing Systems 34, 23359-23373, 2021 | 92 | 2021 |
| Leveraging sparse linear layers for debuggable deep networks E Wong, S Santurkar, A Madry International Conference on Machine Learning, 11205-11216, 2021 | 89 | 2021 |
Aleksander MądryMITPatvirtintas el. paštas mit.edu