Eric Wong
TitelZitiert vonJahr
Provable defenses against adversarial examples via the convex outer adversarial polytope
E Wong, JZ Kolter
arXiv preprint arXiv:1711.00851, 2017
2422017
Provable defenses against adversarial examples via the convex outer adversarial polytope
E Wong, J Zico Kolter
arXiv preprint arXiv:1711.00851, 2017
1582017
Scaling provable adversarial defenses
E Wong, F Schmidt, JH Metzen, JZ Kolter
Advances in Neural Information Processing Systems, 8400-8409, 2018
822018
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
72017
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
E Wong, FR Schmidt, JZ Kolter
arXiv preprint arXiv:1902.07906, 2019
52019
How many random restarts are enough?
T Dick, E Wong, C Dann
Google Scholar, 2014
42014
An SVD and derivative kernel approach to learning from geometric data
E Wong, JZ Kolter
Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
22015
Adversarial Robustness Against the Union of Multiple Perturbation Models
P Maini, E Wong, JZ Kolter
arXiv preprint arXiv:1909.04068, 2019
2019
Neural network inversion beyond gradient descent
E Wong, JZ Kolter
dreaml: A library for dynamic reactive machine learning
E Wong, T Wong, JZ Kolter
Supplement to “A Semismooth Newton Method for Fast, Generic Convex Programming”
A Ali, E Wong, JZ Kolter
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