Wong, Eric

42 publications

NeurIPS 2025 CTSketch: Compositional Tensor Sketching for Scalable Neurosymbolic Learning Seewon Choi, Alaia Solko-Breslin, Rajeev Alur, Eric Wong

ICML 2025 DOLPHIN: A Programmable Framework for Scalable Neurosymbolic Learning Aaditya Naik, Jason Liu, Claire Wang, Amish Sethi, Saikat Dutta, Mayur Naik, Eric Wong

ICLR 2025 Logicbreaks: A Framework for Understanding Subversion of Rule-Based Inference Anton Xue, Avishree Khare, Rajeev Alur, Surbhi Goel, Eric Wong

NeurIPS 2025 Once upon an Input: Reasoning via Per-Instance Program Synthesis Adam Stein, Neelay Velingker, Mayur Naik, Eric Wong

NeurIPS 2025 Probabilistic Stability Guarantees for Feature Attributions Helen Jin, Anton Xue, Weiqiu You, Surbhi Goel, Eric Wong

TMLR 2025 SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas

ICML 2025 Sum-of-Parts: Self-Attributing Neural Networks with End-to-End Learning of Feature Groups Weiqiu You, Helen Qu, Marco Gatti, Bhuvnesh Jain, Eric Wong

DMLR 2025 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

NeurIPS 2024 AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties Xiayan Ji, Anton Xue, Eric Wong, Oleg Sokolsky, Insup Lee

ICMLW 2024 CLAM: Unifying Finetuning, Quantization, and Pruning by Chaining LLM Adapter Modules Neelay Velingker, Jason Liu, Amish Sethi, William Dodds, Zhiqiu Xu, Saikat Dutta, Mayur Naik, Eric Wong

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

NeurIPS 2024 Data-Efficient Learning with Neural Programs Alaia Solko-Breslin, Seewon Choi, Ziyang Li, Neelay Velingker, Rajeev Alur, Mayur Naik, Eric Wong

CVPR 2024 Initialization Matters for Adversarial Transfer Learning Andong Hua, Jindong Gu, Zhiyu Xue, Nicholas Carlini, Eric Wong, Yao Qin

NeurIPS 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

ICMLW 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

NeurIPSW 2024 Logicbreaks: A Framework for Understanding Subversion of Rule-Based Inference Anton Xue, Avishree Khare, Rajeev Alur, Surbhi Goel, Eric Wong

NeurIPSW 2024 Logicbreaks: A Framework for Understanding Subversion of Rule-Based Inference Anton Xue, Avishree Khare, Rajeev Alur, Surbhi Goel, Eric Wong

ICLR 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

ICML 2024 Towards Compositionality in Concept Learning Adam Stein, Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong

CVPR 2023 A Data-Based Perspective on Transfer Learning Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, Aleksander Mądry

WACV 2023 Adversarial Robustness in Discontinuous Spaces via Alternating Sampling & Descent Rahul Venkatesh, Eric Wong, Zico Kolter

ICMLW 2023 Black Box Adversarial Prompting for Foundation Models Natalie Maus, Patrick Chao, Eric Wong, Jacob R. Gardner

ICML 2023 Do Machine Learning Models Learn Statistical Rules Inferred from Data? Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong

NeurIPSW 2023 Jailbreaking Black Box Large Language Models in Twenty Queries Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong

NeurIPSW 2023 Rectifying Group Irregularities in Explanations for Distribution Shift Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik

NeurIPSW 2023 SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks Alexander Robey, Eric Wong, Hamed Hassani, George Pappas

NeurIPS 2023 Stability Guarantees for Feature Attributions with Multiplicative Smoothing Anton Xue, Rajeev Alur, Eric Wong

NeurIPSW 2023 Stability Guarantees for Feature Attributions with Multiplicative Smoothing Anton Xue, Rajeev Alur, Eric Wong

NeurIPSW 2023 Sum-of-Parts Models: Faithful Attributions for Groups of Features Weiqiu You, Helen Qu, Marco Gatti, Bhuvnesh Jain, Eric Wong

NeurIPSW 2023 Visual Topics via Visual Vocabularies Shreya Havaldar, Weiqiu You, Lyle Ungar, Eric Wong

CVPR 2022 Certified Patch Robustness via Smoothed Vision Transformers Hadi Salman, Saachi Jain, Eric Wong, Aleksander Madry

ICLR 2022 Missingness Bias in Model Debugging Saachi Jain, Hadi Salman, Eric Wong, Pengchuan Zhang, Vibhav Vineet, Sai Vemprala, Aleksander Madry

ICLR 2021 Learning Perturbation Sets for Robust Machine Learning Eric Wong, J Zico Kolter

ICML 2021 Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong, Shibani Santurkar, Aleksander Madry

ICML 2020 Adversarial Robustness Against the Union of Multiple Perturbation Models Pratyush Maini, Eric Wong, Zico Kolter

ICLR 2020 Fast Is Better than Free: Revisiting Adversarial Training Eric Wong, Leslie Rice, J. Zico Kolter

ICML 2020 Overfitting in Adversarially Robust Deep Learning Leslie Rice, Eric Wong, Zico Kolter

ICML 2019 Wasserstein Adversarial Examples via Projected Sinkhorn Iterations Eric Wong, Frank Schmidt, Zico Kolter

ICML 2018 Provable Defenses Against Adversarial Examples via the Convex Outer Adversarial Polytope Eric Wong, Zico Kolter

NeurIPS 2018 Scaling Provable Adversarial Defenses Eric Wong, Frank Schmidt, Jan Hendrik Metzen, J. Zico Kolter

ICML 2017 A Semismooth Newton Method for Fast, Generic Convex Programming Alnur Ali, Eric Wong, J. Zico Kolter

AAAI 2015 An SVD and Derivative Kernel Approach to Learning from Geometric Data Eric Wong, J. Zico Kolter