Papers

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

• MDB: Interactively Querying Datasets and Models
  Aaditya Naik, Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik

• 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, Tiny Papers Track (ICLR), 2023

• 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
  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
  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

• 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

• 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

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

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.