An SVD and Derivative Kernel Approach to Learning from Geometric Data. - Eric Wong 001; J. Zico Kolter (AAAI, 2015)

A Semismooth Newton Method for Fast, Generic Convex Programming. - Alnur Ali; Eric Wong 001; J. Zico Kolter (ICML, 2017)

Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope. - Eric Wong 001; J. Zico Kolter (ICML, 2018)

Scaling provable adversarial defenses. - Eric Wong 001; Frank R. Schmidt; Jan Hendrik Metzen; J. Zico Kolter (NeurIPS, 2018)

Wasserstein Adversarial Examples via Projected Sinkhorn Iterations. - Eric Wong 001; Frank R. Schmidt; J. Zico Kolter (ICML, 2019)

Fast is better than free: Revisiting adversarial training. - Eric Wong 001; Leslie Rice; J. Zico Kolter (ICLR, 2020)

Adversarial Robustness Against the Union of Multiple Perturbation Models. - Pratyush Maini; Eric Wong 001; J. Zico Kolter (ICML, 2020)

Overfitting in adversarially robust deep learning. - Leslie Rice; Eric Wong 001; J. Zico Kolter (ICML, 2020)

Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications. - Eric Wong 001; Tim Schneider; Joerg Schmitt; Frank R. Schmidt; J. Zico Kolter (IV, 2020)

Learning perturbation sets for robust machine learning. - Eric Wong 001; J. Zico Kolter (ICLR, 2021)

Leveraging Sparse Linear Layers for Debuggable Deep Networks. - Eric Wong 001; Shibani Santurkar; Aleksander Madry (ICML, 2021)

DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting. - Shaoru Chen; Eric Wong 001; J. Zico Kolter; Mahyar Fazlyab (CoRR, 2021)

Provable, Structured, and Efficient Methods for Robustness of Deep Networks to Adversarial Examples. - Eric Wong 001 (2021)

Certified Patch Robustness via Smoothed Vision Transformers. - Hadi Salman; Saachi Jain; Eric Wong 001; Aleksander Madry (CVPR, 2022)

Missingness Bias in Model Debugging. - Saachi Jain; Hadi Salman; Eric Wong 001; Pengchuan Zhang; Vibhav Vineet; Sai Vemprala; Aleksander Madry (ICLR, 2022)

When does Bias Transfer in Transfer Learning? - Hadi Salman; Saachi Jain; Andrew Ilyas; Logan Engstrom; Eric Wong 001; Aleksander Madry (CoRR, 2022)

A Data-Based Perspective on Transfer Learning. - Saachi Jain; Hadi Salman; Alaa Khaddaj; Eric Wong 001; Sung Min Park; Aleksander Madry (CVPR, 2023)

Comparing Styles across Languages. - Shreya Havaldar; Matthew Pressimone; Eric Wong 001; Lyle H. Ungar (EMNLP, 2023)

TopEx: Topic-based Explanations for Model Comparison. - Shreya Havaldar; Adam Stein; Eric Wong 001; Lyle H. Ungar (Tiny Papers @ ICLR, 2023)

Do Machine Learning Models Learn Statistical Rules Inferred from Data? - Aaditya Naik; Yinjun Wu; Mayur Naik; Eric Wong 001 (ICML, 2023)

Faithful Chain-of-Thought Reasoning. - Qing Lyu 001; Shreya Havaldar; Adam Stein; Li Zhang 039; Delip Rao; Eric Wong 001; Marianna Apidianaki; Chris Callison-Burch (IJCNLP (1), 2023)

Stability Guarantees for Feature Attributions with Multiplicative Smoothing. - Anton Xue; Rajeev Alur; Eric Wong 001 (NeurIPS, 2023)

Adversarial robustness in discontinuous spaces via alternating sampling & descent. - Rahul Mysore Venkatesh; Eric Wong 001; Zico Kolter (WACV, 2023)

Adversarial Prompting for Black Box Foundation Models. - Natalie Maus; Patrick Chao; Eric Wong 001; Jacob R. Gardner (CoRR, 2023)

In-context Example Selection with Influences. - Tai Nguyen 005; Eric Wong 001 (CoRR, 2023)

Do Machine Learning Models Learn Common Sense? - Aaditya Naik; Yinjun Wu; Mayur Naik; Eric Wong 001 (CoRR, 2023)

Rectifying Group Irregularities in Explanations for Distribution Shift. - Adam Stein; Yinjun Wu; Eric Wong 001; Mayur Naik (CoRR, 2023)

MDB: Interactively Querying Datasets and Models. - Aaditya Naik; Adam Stein; Yinjun Wu; Eric Wong 001; Mayur Naik (CoRR, 2023)

Sum-of-Parts Models: Faithful Attributions for Groups of Features. - Weiqiu You; Helen Qu; Marco Gatti; Bhuvnesh Jain; Eric Wong 001 (CoRR, 2023)

Initialization Matters for Adversarial Transfer Learning. - Andong Hua; Jindong Gu; Zhiyu Xue; Nicholas Carlini; Eric Wong 001; Yao Qin 001 (CVPR, 2024)

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation. - Chongyu Fan; Jiancheng Liu; Yihua Zhang; Eric Wong 001; Dennis Wei; Sijia Liu 001 (ICLR, 2024)

Evaluating Groups of Features via Consistency, Contiguity, and Stability. - Chaehyeon Kim; Weiqiu You; Shreya Havaldar; Eric Wong 001 (Tiny Papers @ ICLR, 2024)

Towards Compositionality in Concept Learning. - Adam Stein; Aaditya Naik; Yinjun Wu; Mayur Naik; Eric Wong 001 (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 001 (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 Tramèr; Hamed Hassani; Eric Wong 001 (NeurIPS, 2024)

AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties. - Xiayan Ji; Anton Xue; Eric Wong 001; Oleg Sokolsky; Insup Lee 001 (NeurIPS, 2024)

Data-Efficient Learning with Neural Programs. - Alaia Solko-Breslin; Seewon Choi; Ziyang Li; Neelay Velingker; Rajeev Alur; Mayur Naik; Eric Wong 001 (NeurIPS, 2024)

Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing. - Jiabao Ji; Bairu Hou; Alexander Robey; George J. Pappas; Hamed Hassani; Yang Zhang 001; Eric Wong 001; Shiyu Chang (CoRR, 2024)

Avoiding Copyright Infringement via Machine Unlearning. - Guangyao Dou; Zheyuan Liu 010; Qing Lyu 001; Kaize Ding; Eric Wong 001 (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; et al. (CoRR, 2024)

Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning. - Aaditya Naik; Jason Liu; Claire Wang; Saikat Dutta 001; Mayur Naik; Eric Wong 001 (CoRR, 2024)

TorchQL: A Programming Framework for Integrity Constraints in Machine Learning. - Aaditya Naik; Adam Stein; Yinjun Wu; Mayur Naik; Eric Wong 001 (Proc. ACM Program. Lang., 2024)

Towards Style Alignment in Cross-Cultural Translation. - Shreya Havaldar; Adam Stein; Eric Wong 001; Lyle H. Ungar (ACL (1), 2025)

Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference. - Anton Xue; Avishree Khare; Rajeev Alur; Surbhi Goel; Eric Wong 001 (ICLR, 2025)

Avoiding Copyright Infringement via Large Language Model Unlearning. - Guangyao Dou; Zheyuan Liu 010; Qing Lyu 001; Kaize Ding; Eric Wong 001 (NAACL (Findings), 2025)

Jailbreaking Black Box Large Language Models in Twenty Queries. - Patrick Chao; Alexander Robey; Edgar Dobriban; Hamed Hassani; George J. Pappas; Eric Wong 001 (SaTML, 2025)

SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks. - Alexander Robey; Eric Wong 001; Hamed Hassani; George J. Pappas (Trans. Mach. Learn. Res., 2025)

Where's the Bug? Attention Probing for Scalable Fault Localization. - Adam Stein; Arthur Wayne; Aaditya Naik; Mayur Naik; Eric Wong 001 (CoRR, 2025)

Adaptively evaluating models with task elicitation. - Davis Brown; Prithvi Balehannina; Helen Jin; Shreya Havaldar; Hamed Hassani; Eric Wong 001 (CoRR, 2025)

NSF-SciFy: Mining the NSF Awards Database for Scientific Claims. - Delip Rao; Weiqiu You; Eric Wong 001; Chris Callison-Burch (CoRR, 2025)

CTSketch: Compositional Tensor Sketching for Scalable Neurosymbolic Learning. - Seewon Choi; Alaia Solko-Breslin; Rajeev Alur; Eric Wong 001 (CoRR, 2025)

Probabilistic Stability Guarantees for Feature Attributions. - Helen Jin; Anton Xue; Weiqiu You; Surbhi Goel; Eric Wong 001 (CoRR, 2025)

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models. - Adam Stein; Aaditya Naik; Neelay Velingker; Mayur Naik; Eric Wong 001 (CoRR, 2025)

Benchmarking Misuse Mitigation Against Covert Adversaries. - Davis Brown; Mahdi Sabbaghi; Luze Sun; Alexander Robey; George J. Pappas; Eric Wong 001; Hamed Hassani (CoRR, 2025)

Instruction Following by Boosting Attention of Large Language Models. - Vitoria Guardieiro; Adam Stein; Avishree Khare; Eric Wong 001 (CoRR, 2025)

Probabilistic Soundness Guarantees in LLM Reasoning Chains. - Weiqiu You; Anton Xue; Shreya Havaldar; Delip Rao; Helen Jin; Chris Callison-Burch; Eric Wong 001 (CoRR, 2025)