2024
-
The FIX Benchmark: Extracting Features Interpretable to eXperts
Helen Jin, Shreya Havaldar, Chaehyeon Kim, Anton Xue, Weiqiu You, Helen Qu, Marco Gatti, Daniel A. Hashimoto, Bhuvnesh Jain, Amin Madani, Masao Sako, Lyle Ungar, Eric Wong
Site + Blog Post + Source Code on GitHub -
Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference
Anton Xue, Avishree Khare, Rajeev Alur, Surbhi Goel, Eric Wong
Blog Post + Source Code on GitHub -
Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative
Timothy Bergquist, Johanna Loomba, Emily Pfaff, Fangfang Xia, Zixuan Zhao, Yitan Zhu, Elliot Mitchell, Biplab Bhattacharya, Gaurav Shetty, Tamanna Munia, Grant Delong, Adbul Tariq, Zachary Butzin-Dozier, Yunwen Ji, Haodong Li, Jeremy Coyle, Seraphina Shi, Rachael V. Philips, Andrew Mertens, Romain Pirracchio, Mark van der Laan, John M. Colford Jr., Alan Hubbard, Jifan Gao, Guanhua Chen, Neelay Velingker, Ziyang Li, Yinjun Wu, Adam Stein, Jiani Huang, Zongyu Dai, Qi Long, Mayur Naik, John Holmes, Danielle Mowery, Eric Wong, Ravi Parekh, Emily Getzen, Jake Hightower, Jennifer Blase
eBioMedicine -
Avoiding Copyright Infringement via Machine Unlearning
Guangyao Dou, Zheyuan Liu, Qing Lyu, Kaize Ding, Eric Wong -
Data-Efficient Learning with Neural Programs
Alaia Solko-Breslin, Seewon Choi, Ziyang Li, Neelay Velingker, Rajeev Alur, Mayur Naik, Eric Wong
Blog Post + Source Code on GitHub -
Towards Compositionality in Concept Learning
Adam Stein, Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong
International Conference on Machine learning (ICML), 2024 -
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation
Yinjun Wu, Mayank Keoliya, Kan Chen, Neelay Velingker, Ziyang Li, Emily J Getzen, Qi Long, Mayur Naik, Ravi B Parikh, Eric Wong
International Conference on Machine learning (ICML), 2024 -
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramer, Hamed Hassani, Eric Wong
Site + Source Code on GitHub -
Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing
Jiabao Ji, Bairu Hou, Alexander Robey, George J. Pappas, Hamed Hassani, Yang Zhang, Eric Wong, Shiyu Chang -
Evaluating Groups of Features via Consistency, Contiguity, and Stability
Chaehyeon Kim, Weiqiu You, Shreya Havaldar, Eric Wong
International Conference on Learning Representations (ICLR), 2024 Tiny Papers Track -
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
Chongyu Fan, Jiancheng Liu, Yihua Zhang, Dennis Wei, Eric Wong, Sijia Liu
International Conference on Learning Representations (ICLR), 2024
Source Code on GitHub
2023
-
Initialization Matters for Adversarial Transfer Learning
Andong Hua, Jindong Gu, Zhiyu Xue, Nicholas Carlini, Eric Wong, Yao Qin
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 -
Comparing Styles across Languages
Shreya Havaldar, Matthew Pressimone, Eric Wong, Lyle Ungar
Empirical Methods in Natural Language Processing (EMNLP), 2023 -
Sum-of-Parts Models: Faithful Attributions for Groups of Features
Weiqiu You, Helen Qu, Marco Gatti, Bhuvnesh Jain, Eric Wong
Blog Post + Source Code on GitHub -
Jailbreaking Black Box Large Language Models in Twenty Queries
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong
Blog Post + Source Code on GitHub -
SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks
Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas
Blog Post + Source Code on GitHub -
TorchQL: A Programming Framework for Integrity Constraints in Machine Learning
Aaditya Naik, Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik
Object-oriented Programming, Systems, Languages, and Applications (OOPSLA), 2024
Source Code on GitHub -
Stability Guarantees for Feature Attributions with Multiplicative Smoothing
Anton Xue, Rajeev Alur, Eric Wong
Neural Information Processing Systems (NeurIPS), 2023
Blog Post + Source Code on GitHub -
TopEx: Topic-based Explanations for Model Comparison
Shreya Havaldar, Adam Stein, Eric Wong, Lyle Ungar
International Conference on Learning Representations (ICLR), 2023 Tiny Papers Track -
Rectifying Group Irregularities in Explanations for Distribution Shift
Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik -
Do Machine Learning Models Learn Statistical Rules Inferred from Data?
Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong
International Conference on Machine learning (ICML), 2023
Blog Post + Source Code on GitHub -
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
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
IJCNLP-AACL, 2023
Blog Post + Source Code on GitHub
2022
-
A data-based perspective on transfer learning
Saachi Jain*, Hadi Salman*, Alaa Khaddaj*, Eric Wong, Sung Min Park, Aleksander Madry
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
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
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
IEEE Open Journal of Control Systems (OJCS), 2022 -
Leveraging Sparse Linear Layers for Debuggable Deep Networks
Eric Wong*, Shibani Santurkar*, Aleksander Madry
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
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
International Conference on Machine learning (ICML), 2020
Source Code on GitHub -
Neural network virtual sensors for fuel injection quantities with provable performance specifications
Eric Wong, Tim Schneider, Joerg Schmitt, Frank R. Schmidt, J. Zico Kolter
IEEE Intelligent Vehicles Syimposium (IV), 2020 -
Fast is better than free: revisiting adversarial training
Eric Wong*, Leslie Rice*, J. Zico Kolter
International Conference on Learning Representations (ICLR), 2020
2019
-
Adversarial robustness against the union of multiple perturbation models
Pratyush Maini, Eric Wong, J. Zico Kolter
International Conference on Machine learning (ICML), 2020
Source Code on GitHub -
Wasserstein adversarial examples
Eric Wong, Frank R. Schmidt, J. Zico Kolter
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
Source Code on GitHub
2017
-
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong, J. Zico Kolter
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
International Conference on Machine Learning (ICML), 2017
Source Code on GitHub
2015
- An SVD and Derivative Kernel Approach to Learning from Geometric Data
Eric Wong, J. Zico Kolter
Conference on Artificial Intelligence (AAAI), 2015
Other
My PhD thesis can be found here.