Eric Wong
Title
Cited by
Cited by
Year
Provable defenses against adversarial examples via the convex outer adversarial polytope
E Wong, JZ Kolter
arXiv preprint arXiv:1711.00851, 2017
8722017
Provable defenses against adversarial examples via the convex outer adversarial polytope
E Wong, J Zico Kolter
arXiv preprint arXiv:1711.00851, 2017
8722017
Fast is better than free: Revisiting adversarial training
E Wong, L Rice, JZ Kolter
arXiv preprint arXiv:2001.03994, 2020
2862020
Scaling provable adversarial defenses
E Wong, F Schmidt, JH Metzen, JZ Kolter
Advances in Neural Information Processing Systems, 8400-8409, 2018
2722018
Overfitting in adversarially robust deep learning
L Rice, E Wong, Z Kolter
International Conference on Machine Learning, 8093-8104, 2020
1452020
Wasserstein adversarial examples via projected sinkhorn iterations
E Wong, F Schmidt, Z Kolter
International Conference on Machine Learning, 6808-6817, 2019
922019
Adversarial robustness against the union of multiple perturbation models
P Maini, E Wong, Z Kolter
International Conference on Machine Learning, 6640-6650, 2020
392020
Learning perturbation sets for robust machine learning
E Wong, JZ Kolter
arXiv preprint arXiv:2007.08450, 2020
222020
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
222017
How many random restarts are enough?
T Dick, E Wong, C Dann
Google Scholar, 2014
82014
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
42020
Leveraging Sparse Linear Layers for Debuggable Deep Networks
E Wong, S Santurkar, A Mądry
arXiv preprint arXiv:2105.04857, 2021
32021
An SVD and derivative kernel approach to learning from geometric data
E Wong, JZ Kolter
Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
22015
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting
S Chen, E Wong, JZ Kolter, M Fazlyab
arXiv preprint arXiv:2106.09117, 2021
2021
Classification robust against multiple perturbation types
E Wong, F Schmidt, JZ Kolter, P Maini
US Patent App. 16/857,883, 2020
2020
Provable, structured, and efficient methods for robustness of deep networks to adversarial examples
E Wong
Carnegie Mellon University, 2020
2020
Method, Apparatus and Computer Program for Generating Robust Automated Learning Systems and Testing Trained Automated Learning Systems
E Wong, F Schmidt, JH Metzen, JZ Kolter
US Patent App. 16/173,698, 2019
2019
Semantic Adversarial Robustness with Differentiable Ray-Tracing
R Venkatesh, E Wong, JZ Kolter
Need for Speed Tokyo Drift: Hotwheels Edition (A Drifting Control Case Study)
E Wong, F Chen
CMU-ML-20-102 Provable, structured, and efficient methods for robustness of deep networks to adversarial examples
E Wong
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