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ORCIDEric Wong 0001
Person information
- affiliation: University of Pennsylvania, Department of Computer and Information Science, Philadelphia, PA, USA
- affiliation (former): Massachusetts Institute of Technology (MIT), CSAIL, Cambridge, MA, USA
- affiliation (former, PhD 2020): Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
Other persons with the same name
- Eric Wong — disambiguation page
- Eric Wong 0002 — Georgia Institute of Technology, School of Electrical and Computer Engineering, Atlanta, GA, USA
- Eric Wong 0003 — Industrial Light & Magic, San Francisco, CA, USA (and 1 more)
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2020 – today
- 2024
[i28]Jiabao Ji, Bairu Hou, Alexander Robey, George J. Pappas, Hamed Hassani, Yang Zhang, Eric Wong, Shiyu Chang:
Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing. CoRR abs/2402.16192 (2024)- 2023
- [c20]Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, Aleksander Madry:
A Data-Based Perspective on Transfer Learning. CVPR 2023: 3613-3622 - [c19]Shreya Havaldar, Matthew Pressimone, Eric Wong, Lyle H. Ungar:
Comparing Styles across Languages. EMNLP 2023: 6775-6791 - [c18]Shreya Havaldar, Adam Stein, Eric Wong, Lyle H. Ungar:
TopEx: Topic-based Explanations for Model Comparison. Tiny Papers @ ICLR 2023 - [c17]Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong
:
Do Machine Learning Models Learn Statistical Rules Inferred from Data? ICML 2023: 25677-25693 - [c16]Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, Chris Callison-Burch:
Faithful Chain-of-Thought Reasoning. IJCNLP (1) 2023: 305-329 - [c15]Anton Xue, Rajeev Alur, Eric Wong:
Stability Guarantees for Feature Attributions with Multiplicative Smoothing. NeurIPS 2023 - [c14]Rahul Venkatesh, Eric Wong, Zico Kolter:
Adversarial robustness in discontinuous spaces via alternating sampling & descent. WACV 2023: 4651-4660 - [i27]Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, Chris Callison-Burch:
Faithful Chain-of-Thought Reasoning. CoRR abs/2301.13379 (2023) - [i26]Natalie Maus, Patrick Chao, Eric Wong, Jacob R. Gardner:
Adversarial Prompting for Black Box Foundation Models. CoRR abs/2302.04237 (2023) - [i25]Tai Nguyen, Eric Wong:
In-context Example Selection with Influences. CoRR abs/2302.11042 (2023) - [i24]Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong:
Do Machine Learning Models Learn Common Sense? CoRR abs/2303.01433 (2023) - [i23]Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik:
Rectifying Group Irregularities in Explanations for Distribution Shift. CoRR abs/2305.16308 (2023) - [i22]Shreya Havaldar, Adam Stein, Eric Wong, Lyle H. Ungar:
TopEx: Topic-based Explanations for Model Comparison. CoRR abs/2306.00976 (2023) - [i21]Anton Xue, Rajeev Alur, Eric Wong:
Stability Guarantees for Feature Attributions with Multiplicative Smoothing. CoRR abs/2307.05902 (2023) - [i20]Aaditya Naik, Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik:
MDB: Interactively Querying Datasets and Models. CoRR abs/2308.06686 (2023) - [i19]Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas:
SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks. CoRR abs/2310.03684 (2023) - [i18]Shreya Havaldar, Matthew Pressimone, Eric Wong, Lyle H. Ungar:
Comparing Styles across Languages. CoRR abs/2310.07135 (2023) - [i17]Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong:
Jailbreaking Black Box Large Language Models in Twenty Queries. CoRR abs/2310.08419 (2023) - [i16]Chongyu Fan, Jiancheng Liu, Yihua Zhang, Dennis Wei, Eric Wong, Sijia Liu:
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation. CoRR abs/2310.12508 (2023) - [i15]Weiqiu You, Helen Qu, Marco Gatti, Bhuvnesh Jain, Eric Wong:
Sum-of-Parts Models: Faithful Attributions for Groups of Features. CoRR abs/2310.16316 (2023) - 2022
- [c13]Hadi Salman, Saachi Jain, Eric Wong, Aleksander Madry:
Certified Patch Robustness via Smoothed Vision Transformers. CVPR 2022: 15116-15126 - [c12]Saachi Jain, Hadi Salman, Eric Wong, Pengchuan Zhang, Vibhav Vineet, Sai Vemprala, Aleksander Madry:
Missingness Bias in Model Debugging. ICLR 2022 - [i14]Saachi Jain, Hadi Salman, Eric Wong, Pengchuan Zhang, Vibhav Vineet, Sai Vemprala, Aleksander Madry:
Missingness Bias in Model Debugging. CoRR abs/2204.08945 (2022) - [i13]Hadi Salman, Saachi Jain, Andrew Ilyas, Logan Engstrom, Eric Wong, Aleksander Madry:
When does Bias Transfer in Transfer Learning? CoRR abs/2207.02842 (2022) - [i12]Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, Aleksander Madry:
A Data-Based Perspective on Transfer Learning. CoRR abs/2207.05739 (2022) - 2021
- [b1]Eric Wong:
Provable, Structured, and Efficient Methods for Robustness of Deep Networks to Adversarial Examples. Carnegie Mellon University, USA, 2021 - [c11]Eric Wong, J. Zico Kolter:
Learning perturbation sets for robust machine learning. ICLR 2021 - [c10]Eric Wong, Shibani Santurkar, Aleksander Madry:
Leveraging Sparse Linear Layers for Debuggable Deep Networks. ICML 2021: 11205-11216 - [i11]Eric Wong, Shibani Santurkar, Aleksander Madry:
Leveraging Sparse Linear Layers for Debuggable Deep Networks. CoRR abs/2105.04857 (2021) - [i10]Shaoru Chen, Eric Wong, J. Zico Kolter, Mahyar Fazlyab:
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting. CoRR abs/2106.09117 (2021) - [i9]Hadi Salman, Saachi Jain, Eric Wong, Aleksander Madry:
Certified Patch Robustness via Smoothed Vision Transformers. CoRR abs/2110.07719 (2021) - 2020
- [c9]Eric Wong, Leslie Rice, J. Zico Kolter:
Fast is better than free: Revisiting adversarial training. ICLR 2020 - [c8]Pratyush Maini, Eric Wong, J. Zico Kolter:
Adversarial Robustness Against the Union of Multiple Perturbation Models. ICML 2020: 6640-6650 - [c7]Leslie Rice, Eric Wong, J. Zico Kolter:
Overfitting in adversarially robust deep learning. ICML 2020: 8093-8104 - [c6]Eric Wong, Tim Schneider, Joerg Schmitt, Frank R. Schmidt, J. Zico Kolter:
Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications. IV 2020: 1753-1758 - [i8]Eric Wong, Leslie Rice, J. Zico Kolter:
Fast is better than free: Revisiting adversarial training. CoRR abs/2001.03994 (2020) - [i7]Leslie Rice, Eric Wong, J. Zico Kolter:
Overfitting in adversarially robust deep learning. CoRR abs/2002.11569 (2020) - [i6]Eric Wong, Tim Schneider, Joerg Schmitt, Frank R. Schmidt, J. Zico Kolter:
Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications. CoRR abs/2007.00147 (2020) - [i5]Eric Wong, J. Zico Kolter:
Learning perturbation sets for robust machine learning. CoRR abs/2007.08450 (2020)
2010 – 2019
- 2019
- [c5]Eric Wong, Frank R. Schmidt, J. Zico Kolter:
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations. ICML 2019: 6808-6817 - [i4]Eric Wong, Frank R. Schmidt, J. Zico Kolter:
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations. CoRR abs/1902.07906 (2019) - [i3]Pratyush Maini, Eric Wong, J. Zico Kolter:
Adversarial Robustness Against the Union of Multiple Perturbation Models. CoRR abs/1909.04068 (2019) - 2018
- [c4]Eric Wong, J. Zico Kolter:
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope. ICML 2018: 5283-5292 - [c3]Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen, J. Zico Kolter:
Scaling provable adversarial defenses. NeurIPS 2018: 8410-8419 - [i2]Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen, J. Zico Kolter:
Scaling provable adversarial defenses. CoRR abs/1805.12514 (2018) - 2017
- [c2]Alnur Ali, Eric Wong, J. Zico Kolter:
A Semismooth Newton Method for Fast, Generic Convex Programming. ICML 2017: 70-79 - [i1]J. Zico Kolter, Eric Wong:
Provable defenses against adversarial examples via the convex outer adversarial polytope. CoRR abs/1711.00851 (2017) - 2015
- [c1]Eric Wong, J. Zico Kolter:
An SVD and Derivative Kernel Approach to Learning from Geometric Data. AAAI 2015: 1889-1895
Coauthor Index
aka: Zico Kolter
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