Eric Wong

According to our database, Eric Wong authored at least 44 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

TorchQL: A Programming Framework for Integrity Constraints in Machine Learning.

Aaditya Naik, Adam Stein, Yinjun Wu, Mayur Naik, Eric Wong

Proc. ACM Program. Lang., 2024

Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning.

Aaditya Naik, Jason Liu, Claire Wang, Saikat Dutta, Mayur Naik, Eric Wong

CoRR, 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 H. Ungar, Eric Wong

CoRR, 2024

Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference.

Anton Xue, Avishree Khare, Rajeev Alur, Surbhi Goel, Eric Wong

CoRR, 2024

Avoiding Copyright Infringement via Machine Unlearning.

Guangyao Dou, Zheyuan Liu, Qing Lyu, Kaize Ding, Eric Wong

CoRR, 2024

Data-Efficient Learning with Neural Programs.

Alaia Solko-Breslin, Seewon Choi, Ziyang Li, Neelay Velingker, Rajeev Alur, Mayur Naik, Eric Wong

CoRR, 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 Tramèr, Hamed Hassani, Eric Wong

CoRR, 2024

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

CoRR, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Towards Compositionality in Concept Learning.

Adam Stein, Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Evaluating Groups of Features via Consistency, Contiguity, and Stability.

Chaehyeon Kim, Weiqiu You, Shreya Havaldar, Eric Wong

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation.

Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, Sijia Liu

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Initialization Matters for Adversarial Transfer Learning.

Andong Hua, Jindong Gu, Zhiyu Xue, Nicholas Carlini, Eric Wong, Yao Qin

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Sum-of-Parts Models: Faithful Attributions for Groups of Features.

Weiqiu You, Helen Qu, Marco Gatti, Bhuvnesh Jain, Eric Wong

CoRR, 2023

Jailbreaking Black Box Large Language Models in Twenty Queries.

Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong

CoRR, 2023

SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks.

Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas

CoRR, 2023

MDB: Interactively Querying Datasets and Models.

Aaditya Naik, Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik

CoRR, 2023

Rectifying Group Irregularities in Explanations for Distribution Shift.

Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik

CoRR, 2023

Do Machine Learning Models Learn Common Sense?

Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong

CoRR, 2023

In-context Example Selection with Influences.

Tai Nguyen, Eric Wong

CoRR, 2023

Adversarial Prompting for Black Box Foundation Models.

Natalie Maus, Patrick Chao, Eric Wong, Jacob R. Gardner

CoRR, 2023

Adversarial robustness in discontinuous spaces via alternating sampling & descent.

Rahul Venkatesh, Eric Wong, Zico Kolter

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Stability Guarantees for Feature Attributions with Multiplicative Smoothing.

Anton Xue, Rajeev Alur, Eric Wong

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Faithful Chain-of-Thought Reasoning.

Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, Chris Callison-Burch

Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, 2023

Do Machine Learning Models Learn Statistical Rules Inferred from Data?

Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong

Proceedings of the International Conference on Machine Learning, 2023

TopEx: Topic-based Explanations for Model Comparison.

Shreya Havaldar, Adam Stein, Eric Wong, Lyle H. Ungar

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

Comparing Styles across Languages.

Shreya Havaldar, Matthew Pressimone, Eric Wong, Lyle H. Ungar

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

A Data-Based Perspective on Transfer Learning.

Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, Aleksander Madry

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

When does Bias Transfer in Transfer Learning?

Hadi Salman, Saachi Jain, Andrew Ilyas, Logan Engstrom, Eric Wong, Aleksander Madry

CoRR, 2022

Missingness Bias in Model Debugging.

Saachi Jain, Hadi Salman, Eric Wong, Pengchuan Zhang, Vibhav Vineet, Sai Vemprala, Aleksander Madry

Proceedings of the Tenth International Conference on Learning Representations, 2022

Certified Patch Robustness via Smoothed Vision Transformers.

Hadi Salman, Saachi Jain, Eric Wong, Aleksander Madry

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Provable, Structured, and Efficient Methods for Robustness of Deep Networks to Adversarial Examples.

Eric Wong

PhD thesis, 2021

DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting.

Shaoru Chen, Eric Wong, J. Zico Kolter, Mahyar Fazlyab

CoRR, 2021

Leveraging Sparse Linear Layers for Debuggable Deep Networks.

Eric Wong, Shibani Santurkar, Aleksander Madry

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning perturbation sets for robust machine learning.

Eric Wong, J. Zico Kolter

Proceedings of the 9th International Conference on Learning Representations, 2021

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

Proceedings of the IEEE Intelligent Vehicles Symposium, 2020

Overfitting in adversarially robust deep learning.

Leslie Rice, Eric Wong, J. Zico Kolter

Proceedings of the 37th International Conference on Machine Learning, 2020

Adversarial Robustness Against the Union of Multiple Perturbation Models.

Pratyush Maini, Eric Wong, J. Zico Kolter

Proceedings of the 37th International Conference on Machine Learning, 2020

Fast is better than free: Revisiting adversarial training.

Eric Wong, Leslie Rice, J. Zico Kolter

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Wasserstein Adversarial Examples via Projected Sinkhorn Iterations.

Eric Wong, Frank R. Schmidt, J. Zico Kolter

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Scaling provable adversarial defenses.

Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen, J. Zico Kolter

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope.

Eric Wong, J. Zico Kolter

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

A Semismooth Newton Method for Fast, Generic Convex Programming.

Alnur Ali, Eric Wong, J. Zico Kolter

Proceedings of the 34th International Conference on Machine Learning, 2017

2015

An SVD and Derivative Kernel Approach to Learning from Geometric Data.

Eric Wong, J. Zico Kolter

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015