2023
-
Rectifying Group Irregularities in Explanations for Distribution Shift
Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik -
Do Machine Learning Models Learn Common Sense?
Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong
In Proceedings of the International Conference on Machine learning (ICML), 2023 -
In-context Example Selection with Influences
Tai Nguyen, Eric Wong
Blog Post + Source Code on GitHub -
Adversarial Prompting for Black Box Foundation Models
Natalie Maus*, Patrick Chao*, Eric Wong, Jacob Gardner
Keynote in DLSP 2023
Blog Post + Source Code on GitHub -
Faithful Chain-of-Thought Reasoning
Qing Lyu*, Shreya Havaldar*, Adam Stein*, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, Chris Callison-Burch
2022
-
A data-based perspective on transfer learning
Saachi Jain*, Hadi Salman*, Alaa Khaddaj*, Eric Wong, Sung Min Park, Aleksander Madry
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Blog Post + Source Code on GitHub -
When does bias transfer in transfer learning
Hadi Salman*, Saachi Jain*, Andrew Ilyas*, Logan Engstrom*, Eric Wong, Aleksander Madry
Blog Post + Source Code on GitHub -
Missingness bias in model debugging
Saachi Jain*, Hadi Salman*, Pengchuan Zhang, Vibhav Vineet, Sal Vemprala, Aleksander Madry
In proceedings of the International Conference on Learning Representations (ICLR), 2022
Blog Post + Source Code on GitHub
2021
-
Certified patch robustness via smoothed vision transformers
Hadi Salman*, Saachi Jain*, Eric Wong*, Aleksander Madry
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
Blog Post + Source Code on GitHub -
DeepSplit: Scalable verification of deep neural networks via operator splitting
Shaoru Chen*, Eric Wong*, J. Zico Kolter, Mahyar Fazlyab
In Proceedings of the IEEE Open Journal of Control Systems (OJCS), 2022 -
Leveraging Sparse Linear Layers for Debuggable Deep Networks
Eric Wong*, Shibani Santurkar*, Aleksander Madry
In Proceedings of the International Conference on Machine learning (ICML), 2021 Long Oral
Blog Post + Source Code on GitHub
2020
-
Learning perturbation sets for robust machine learning
Eric Wong, J. Zico Kolter
In proceedings of the International Conference on Learning Representations (ICLR), 2021
Blog Post + Source Code on GitHub -
Overfitting in adversarially robust deep learning
Leslie Rice*, Eric Wong*, J. Zico Kolter
In Proceedings of the International Conference on Machine learning (ICML), 2020 -
Neural network virtual sensors for fuel injection quantities with provable performance specifications
Eric Wong, Tim Schneider, Joerg Schmitt, Frank R. Schmidt, J. Zico Kolter
In Proceedings of the IEEE Intelligent Vehicles Syimposium (IV), 2020 -
Fast is better than free: revisiting adversarial training
Eric Wong*, Leslie Rice*, J. Zico Kolter
In Proceedings of the International Conference on Learning Representations (ICLR), 2020
2019
-
Adversarial robustness against the union of multiple perturbation models
Pratyush Maini, Eric Wong, J. Zico Kolter
In Proceedings of the International Conference on Machine learning (ICML), 2020 -
Wasserstein adversarial examples
Eric Wong, Frank R. Schmidt, J. Zico Kolter
In Proceedings of the International Conference on Machine Learning (ICML), 2019
2018
-
Scaling provable adversarial defenses
Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen, J. Zico Kolter
In Neural Information Processing Systems (NeurIPS), 2018
2017
-
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong, J. Zico Kolter
In Proceedings of the International Conference on Machine Learning (ICML), 2018; Best defense paper at NIPS 2017 ML & Security Workshop
Blog Post + Source Code on GitHub -
A Semismooth Newton Method for Fast, Generic Convex Programming
Alnur Ali*, Eric Wong*, J. Zico Kolter
In Proceedings of the International Conference on Machine Learning (ICML), 2017
2015
-
An SVD and Derivative Kernel Approach to Learning from Geometric Data
Eric Wong, J. Zico Kolter
In Proceedings of the Conference on Artificial Intelligence (AAAI), 2015
Other
My PhD thesis can be found here.