| Structured learning of sum-of-submodular higher order energy functions A Fix, T Joachims, SM Park, R Zabih Proceedings of the IEEE International Conference on Computer Vision, 3104-3111, 2013 | 29 | 2013 |
| Datamodels: Predicting predictions from training data A Ilyas, SM Park, L Engstrom, G Leclerc, A Madry International Conference on Machine Learning, 9525-9587, 2022 | 26* | 2022 |
| A data-based perspective on transfer learning S Jain, H Salman, A Khaddaj, E Wong, SM Park, A Madry arXiv preprint arXiv:2207.05739, 2022 | 4 | 2022 |
| Sparse PCA from sparse linear regression G Bresler, SM Park, M Persu Advances in Neural Information Processing Systems 31, 2018 | 4 | 2018 |
| ModelDiff: A Framework for Comparing Learning Algorithms H Shah, SM Park, A Ilyas, A Madry arXiv preprint arXiv:2211.12491, 2022 | 1 | 2022 |
| FFCV: Accelerating Training by Removing Data Bottlenecks G Leclerc, A Ilyas, L Engstrom, SM Park, H Salman, A Madry | 1 | 2022 |
| On distinctive properties of universal perturbations SM Park, KA Wei, K Xiao, J Li, A Madry arXiv preprint arXiv:2112.15329, 2021 | 1 | 2021 |
| On the equivalence of sparse statistical problems SM Park Massachusetts Institute of Technology, 2016 | | 2016 |
| Non-robust Features through the Lens of Universal Perturbations SM Park, KA Wei, KY Xiao, J Li, A Madry | | |
| A Unified Framework for Comparing Learning Algorithms H Shah, SM Park, A Ilyas, A Madry NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and …, 0 | | |
Aleksander MądryMITVerified email at mit.edu
