| Towards deep learning models resistant to adversarial attacks A Madry, A Makelov, L Schmidt, D Tsipras, A Vladu arXiv preprint arXiv:1706.06083, 2017 | 7598 | 2017 |
| Do ImageNet Classifiers Generalize to ImageNet? B Recht, R Roelofs, L Schmidt, V Shankar arXiv preprint arXiv:1902.10811, 2019 | 1010* | 2019 |
| Exploring the Landscape of Spatial Robustness L Engstrom, B Tran, D Tsipras, L Schmidt, A Madry International Conference on Machine Learning, 1802-1811, 2019 | 641* | 2019 |
| Adversarially robust generalization requires more data L Schmidt, S Santurkar, D Tsipras, K Talwar, A Madry Advances in Neural Information Processing Systems 31, 5014-5026, 2018 | 623 | 2018 |
| Unlabeled data improves adversarial robustness Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang Advances in Neural Information Processing Systems, 11192-11203, 2019 | 497 | 2019 |
| Practical and optimal LSH for angular distance A Andoni, P Indyk, T Laarhoven, I Razenshteyn, L Schmidt Advances in Neural Information Processing Systems, 1225-1233, 2015 | 444 | 2015 |
| Measuring robustness to natural distribution shifts in image classification R Taori, A Dave, V Shankar, N Carlini, B Recht, L Schmidt | 249 | 2020 |
| Recent developments in the sparse Fourier transform: A compressed Fourier transform for big data AC Gilbert, P Indyk, M Iwen, L Schmidt IEEE Signal Processing Magazine 31 (5), 91-100, 2014 | 176 | 2014 |
| A meta-analysis of overfitting in machine learning R Roelofs, S Fridovich-Keil, J Miller, V Shankar, M Hardt, B Recht, ... Proceedings of the 33rd International Conference on Neural Information …, 2019 | 113 | 2019 |
| Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time M Wortsman, G Ilharco, SY Gadre, R Roelofs, R Gontijo-Lopes, ... International Conference on Machine Learning, 23965-23998, 2022 | 112 | 2022 |
| Model reconstruction from model explanations S Milli, L Schmidt, AD Dragan, M Hardt Proceedings of the Conference on Fairness, Accountability, and Transparency, 1-9, 2019 | 110 | 2019 |
| Approximation algorithms for model-based compressive sensing C Hegde, P Indyk, L Schmidt IEEE Transactions on Information Theory 61 (9), 5129-5147, 2015 | 107* | 2015 |
| A nearly-linear time framework for graph-structured sparsity C Hegde, P Indyk, L Schmidt International Conference on Machine Learning, 928-937, 2015 | 105 | 2015 |
| Robust fine-tuning of zero-shot models M Wortsman, G Ilharco, JW Kim, M Li, S Kornblith, R Roelofs, RG Lopes, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 98 | 2022 |
| Retiring Adult: New Datasets for Fair Machine Learning F Ding, M Hardt, J Miller, L Schmidt Advances in Neural Information Processing Systems 34, 2021 | 97 | 2021 |
| On the limitations of first order approximation in GAN dynamics J Li, A Madry, J Peebles, L Schmidt | 92* | 2018 |
| Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization JP Miller, R Taori, A Raghunathan, S Sagawa, PW Koh, V Shankar, ... International Conference on Machine Learning, 7721-7735, 2021 | 88 | 2021 |
| Evaluating Machine Accuracy on ImageNet V Shankar, R Roelofs, H Mania, A Fang, B Recht, L Schmidt International Conference on Machine Learning (ICML), 2020 | 85 | 2020 |
| Sample-optimal density estimation in nearly-linear time J Acharya, I Diakonikolas, J Li, L Schmidt Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017 | 85 | 2017 |
| The effect of natural distribution shift on question answering models J Miller, K Krauth, B Recht, L Schmidt International Conference on Machine Learning, 6905-6916, 2020 | 81 | 2020 |
Aleksander MądryMITVerified email at mit.edu
