| Provable defenses against adversarial examples via the convex outer adversarial polytope E Wong, JZ Kolter arXiv preprint arXiv:1711.00851, 2017 | 1252 | 2017 |
| Provable defenses against adversarial examples via the convex outer adversarial polytope E Wong, J Zico Kolter arXiv preprint arXiv:1711.00851, 2017 | 1252 | 2017 |
| Fast is better than free: Revisiting adversarial training E Wong, L Rice, JZ Kolter arXiv preprint arXiv:2001.03994, 2020 | 727 | 2020 |
| Overfitting in adversarially robust deep learning L Rice, E Wong, Z Kolter International Conference on Machine Learning, 8093-8104, 2020 | 450 | 2020 |
| Scaling provable adversarial defenses E Wong, F Schmidt, JH Metzen, JZ Kolter Advances in Neural Information Processing Systems, 8400-8409, 2018 | 384 | 2018 |
| Wasserstein adversarial examples via projected sinkhorn iterations E Wong, F Schmidt, Z Kolter International Conference on Machine Learning, 6808-6817, 2019 | 172 | 2019 |
| Adversarial robustness against the union of multiple perturbation models P Maini, E Wong, Z Kolter International Conference on Machine Learning, 6640-6650, 2020 | 101 | 2020 |
| Learning perturbation sets for robust machine learning E Wong, JZ Kolter arXiv preprint arXiv:2007.08450, 2020 | 51 | 2020 |
| Leveraging sparse linear layers for debuggable deep networks E Wong, S Santurkar, A Madry International Conference on Machine Learning, 11205-11216, 2021 | 38 | 2021 |
| A semismooth Newton method for fast, generic convex programming A Ali, E Wong, JZ Kolter Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 24 | 2017 |
| 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 | 20 | 2022 |
| How many random restarts are enough? T Dick, E Wong, C Dann Google Scholar, 2014 | 12 | 2014 |
| Missingness Bias in Model Debugging S Jain, H Salman, E Wong, P Zhang, V Vineet, S Vemprala, A Madry International Conference on Learning Representations, 2021 | 8 | 2021 |
| Deepsplit: Scalable verification of deep neural networks via operator splitting S Chen, E Wong, JZ Kolter, M Fazlyab IEEE Open Journal of Control Systems 1, 126-140, 2022 | 7 | 2022 |
| Neural network virtual sensors for fuel injection quantities with provable performance specifications E Wong, T Schneider, J Schmitt, FR Schmidt, JZ Kolter 2020 IEEE Intelligent Vehicles Symposium (IV), 1753-1758, 2020 | 6 | 2020 |
| 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 |
| 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 | 3 | 2022 |
| Neural network inversion beyond gradient descent E Wong, JZ Kolter Advances in Neural Information Processing Systems, Workshop on Optimization …, 2017 | 2 | 2017 |
| An SVD and derivative kernel approach to learning from geometric data E Wong, JZ Kolter Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015 | 2 | 2015 |
| New Frontiers in Adversarial Machine Learning S Liu, PY Chen, D Zhu, E Wong, K Grosse, H Lakkaraju, S Koyejo International Conference on Machine Learning, 2022 | | 2022 |
