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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)
Other persons with a similar name
- Eric C. Wong — University of California, San Diego, Departments of Radiology and Psychiatry, CA, USA
- Eric Chi-Wah Wong — CANSTAR Inc., Toronto, ON, Canada
- Eric Kwok Chun Wong
- Eric Siu Wing Wong
- Eric T. T. Wong
![[0000-0001-5382-6023]](/https://anthro-https-dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-5382-6023")
(aka: Eric Tsun Tat Wong, T. T. Wong 0001) — Hong Kong Polytechnic University, Department of Mechanical Engineering, Hong Kong - Eric Wing Ming Wong (aka: Eric W. M. Wong) — City University of Hong Kong
- Hin Kit Eric Wong
- W. Eric Wong — University of Texas at Dallas, Department of Computer Science, TX, USA
- Yanyi Liu Wong (aka: Eric Liu Wong) — University of Maryland, Department of Electrical and Computer Engi-neering, College Park, MD, USA (and 1 more)
- Eric T. Wong 0001 — Beth Israel Deaconess Medical Center, Boston, MA, USA
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2020 – today
- 2026
[c42]Delip Rao, Weiqiu You, Eric Wong, Chris Callison-Burch:
NSF-SciFy: Mining the NSF Awards Database for Scientific Claims. ACL (1) 2026: 45679-45697- [i61]Luze Sun, Alina Oprea, Eric Wong:
Semantics-Preserving Evasion of LLM Vulnerability Detectors. CoRR abs/2602.00305 (2026) - [i60]Neelay Velingker, Alaia Solko-Breslin, Mayank Keoliya, Seewon Choi, Jiayi Xin, Anika Marathe, Alireza Oraii, Rajat Deo, Sameed Ahmed M. Khatana, Rajeev Alur, Mayur Naik, Eric Wong:
CAMEL: An ECG Language Model for Forecasting Cardiac Events. CoRR abs/2602.15677 (2026) - [i59]Shailesh Sridhar, Anton Xue, Eric Wong:
Missingness Bias Calibration in Feature Attribution Explanations. CoRR abs/2603.04831 (2026) - [i58]Delip Rao, Eric Wong, Chris Callison-Burch:
Detecting and Correcting Reference Hallucinations in Commercial LLMs and Deep Research Agents. CoRR abs/2604.03173 (2026) - [i57]Adam Stein, Davis Brown, Hamed Hassani, Mayur Naik, Eric Wong:
Detecting Safety Violations Across Many Agent Traces. CoRR abs/2604.11806 (2026) - [i56]Weiqiu You, Cassandra Goldberg, Amin Madani, Daniel A. Hashimoto, Eric Wong:
Sum-of-Checks: Structured Reasoning for Surgical Safety with Large Vision-Language Models. CoRR abs/2604.22156 (2026) - [i55]Davis Brown, Samarth Bhargav, Arav Santhanam, Kasper Hong, Ivan Zhang, Matan Shtepel, Steffi Chern, Alexander Robey, Eric Wong, Hamed Hassani:
Stateful Online Monitoring Catches Distributed Agent Attacks. CoRR abs/2605.31593 (2026) - 2025
- [j3]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:
The FIX Benchmark: Extracting Features Interpretable to eXperts. J. Data-centric Mach. Learn. Res. 2: (17):1-43 (2025) - [j2]Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas:
SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks. Trans. Mach. Learn. Res. 2025 (2025) - [c41]Shreya Havaldar, Adam Stein, Eric Wong, Lyle H. Ungar:
Towards Style Alignment in Cross-Cultural Translation. ACL (1) 2025: 32213-32230 - [c40]Weiqiu You, Anton Xue, Shreya Havaldar, Delip Rao, Helen Jin, Chris Callison-Burch, Eric Wong:
Probabilistic Soundness Guarantees in LLM Reasoning Chains. EMNLP 2025: 7506-7525 - [c39]Andong Hua, Kenan Tang, Chenhe Gu, Jindong Gu, Eric Wong, Yao Qin:
Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs. EMNLP 2025: 19889-19899 - [c38]Davis Brown, Prithvi Balehannina, Helen Jin, Shreya Havaldar, Hamed Hassani, Eric Wong:
Adaptively profiling models with task elicitation. EMNLP 2025: 24985-25020 - [c37]Anton Xue, Avishree Khare, Rajeev Alur, Surbhi Goel, Eric Wong:
Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference. ICLR 2025 - [c36]Aaditya Naik, Jason Liu, Claire Wang, Amish Sethi, Saikat Dutta, Mayur Naik, Eric Wong:
DOLPHIN: A Programmable Framework for Scalable Neurosymbolic Learning. ICML 2025 - [c35]Weiqiu You, Helen Qu, Marco Gatti, Bhuvnesh Jain, Eric Wong:
Sum-of-Parts: Self-Attributing Neural Networks with End-to-End Learning of Feature Groups. ICML 2025 - [c34]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. IJCNLP-AACL (Short Papers) 2025: 7-40 - [c33]Guangyao Dou, Zheyuan Liu, Qing Lyu, Kaize Ding, Eric Wong:
Avoiding Copyright Infringement via Large Language Model Unlearning. NAACL (Findings) 2025: 5176-5200 - [c32]Seewon Choi, Alaia Solko-Breslin, Rajeev Alur, Eric Wong:
CTSketch: Compositional Tensor Sketching for Scalable Neurosymbolic Learning. NeurIPS 2025 - [c31]Adam Stein, Neelay Velingker, Mayur Naik, Eric Wong:
Once Upon an Input: Reasoning via Per-Instance Program Synthesis. NeurIPS 2025 - [c30]Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong:
Jailbreaking Black Box Large Language Models in Twenty Queries. SaTML 2025: 23-42 - [i54]Adam Stein, Arthur Wayne, Aaditya Naik, Mayur Naik, Eric Wong:
Where's the Bug? Attention Probing for Scalable Fault Localization. CoRR abs/2502.13966 (2025) - [i53]Davis Brown, Prithvi Balehannina, Helen Jin, Shreya Havaldar, Hamed Hassani, Eric Wong:
Adaptively evaluating models with task elicitation. CoRR abs/2503.01986 (2025) - [i52]Delip Rao, Weiqiu You, Eric Wong, Chris Callison-Burch:
NSF-SciFy: Mining the NSF Awards Database for Scientific Claims. CoRR abs/2503.08600 (2025) - [i51]Seewon Choi, Alaia Solko-Breslin, Rajeev Alur, Eric Wong:
CTSketch: Compositional Tensor Sketching for Scalable Neurosymbolic Learning. CoRR abs/2503.24123 (2025) - [i50]Helen Jin, Anton Xue, Weiqiu You, Surbhi Goel, Eric Wong:
Probabilistic Stability Guarantees for Feature Attributions. CoRR abs/2504.13787 (2025) - [i49]Adam Stein, Aaditya Naik, Neelay Velingker, Mayur Naik, Eric Wong:
The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models. CoRR abs/2505.24874 (2025) - [i48]Davis Brown, Mahdi Sabbaghi, Luze Sun, Alexander Robey, George J. Pappas, Eric Wong, Hamed Hassani:
Benchmarking Misuse Mitigation Against Covert Adversaries. CoRR abs/2506.06414 (2025) - [i47]Vitoria Guardieiro, Adam Stein, Avishree Khare, Eric Wong:
Instruction Following by Boosting Attention of Large Language Models. CoRR abs/2506.13734 (2025) - [i46]Shreya Havaldar, Adam Stein, Eric Wong, Lyle H. Ungar:
Towards Style Alignment in Cross-Cultural Translation. CoRR abs/2507.00216 (2025) - [i45]Weiqiu You, Anton Xue, Shreya Havaldar, Delip Rao, Helen Jin, Chris Callison-Burch, Eric Wong:
Probabilistic Soundness Guarantees in LLM Reasoning Chains. CoRR abs/2507.12948 (2025) - [i44]Andong Hua, Kenan Tang, Chenhe Gu, Jindong Gu, Eric Wong, Yao Qin:
Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs. CoRR abs/2509.01790 (2025) - [i43]Sagnik Anupam, Davis Brown, Shuo Li, Eric Wong, Hamed Hassani, Osbert Bastani:
BrowserArena: Evaluating LLM Agents on Real-World Web Navigation Tasks. CoRR abs/2510.02418 (2025) - [i42]Mayank Keoliya, Seewon Choi, Rajeev Alur, Mayur Naik, Eric Wong:
Stable Prediction of Adverse Events in Medical Time-Series Data. CoRR abs/2510.14286 (2025) - [i41]Adam Stein, Neelay Velingker, Mayur Naik, Eric Wong:
Once Upon an Input: Reasoning via Per-Instance Program Synthesis. CoRR abs/2510.22849 (2025) - [i40]Shreya Havaldar, Helen Jin, Chaehyeon Kim, Anton Xue, Weiqiu You, Marco Gatti, Bhuvnesh Jain, Helen Qu, Daniel A. Hashimoto, Amin Madani, Rajat Deo, Sameed Ahmed M. Khatana, Gary E. Weissman, Lyle H. Ungar, Eric Wong:
T-FIX: Text-Based Explanations with Features Interpretable to eXperts. CoRR abs/2511.04070 (2025) - [i39]Cassandra Goldberg, Chaehyeon Kim, Adam Stein, Eric Wong:
SuperActivators: Only the Tail of the Distribution Contains Reliable Concept Signals. CoRR abs/2512.05038 (2025) - 2024
- [j1]Aaditya Naik, Adam Stein, Yinjun Wu, Mayur Naik, Eric Wong:
TorchQL: A Programming Framework for Integrity Constraints in Machine Learning. Proc. ACM Program. Lang. 8(OOPSLA1): 833-863 (2024) - [c29]Andong Hua, Jindong Gu, Zhiyu Xue, Nicholas Carlini, Eric Wong, Yao Qin:
Initialization Matters for Adversarial Transfer Learning. CVPR 2024: 24831-24840 - [c28]Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, Sijia Liu:
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation. ICLR 2024 - [c27]Chaehyeon Kim, Weiqiu You, Shreya Havaldar, Eric Wong:
Evaluating Groups of Features via Consistency, Contiguity, and Stability. Tiny Papers @ ICLR 2024 - [c26]Adam Stein, Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong:
Towards Compositionality in Concept Learning. ICML 2024: 46530-46555 - [c25]Yinjun Wu, Mayank Keoliya, Kan Chen, Neelay Velingker, Ziyang Li, Emily J. Getzen, Qi Long, Mayur Naik, Ravi B. Parikh, Eric Wong:
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation. ICML 2024: 53597-53618 - [c24]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:
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. NeurIPS 2024 - [c23]Xiayan Ji, Anton Xue, Eric Wong, Oleg Sokolsky, Insup Lee:
AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties. NeurIPS 2024 - [c22]Alaia Solko-Breslin, Seewon Choi, Ziyang Li, Neelay Velingker, Rajeev Alur, Mayur Naik, Eric Wong:
Data-Efficient Learning with Neural Programs. NeurIPS 2024 - [i38]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) - [i37]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:
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. CoRR abs/2404.01318 (2024) - [i36]Yinjun Wu, Mayank Keoliya, Kan Chen, Neelay Velingker, Ziyang Li, Emily J. Getzen, Qi Long, Mayur Naik, Ravi B. Parikh, Eric Wong:
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation. CoRR abs/2406.00611 (2024) - [i35]Alaia Solko-Breslin, Seewon Choi, Ziyang Li, Neelay Velingker, Rajeev Alur, Mayur Naik, Eric Wong:
Data-Efficient Learning with Neural Programs. CoRR abs/2406.06246 (2024) - [i34]Guangyao Dou, Zheyuan Liu, Qing Lyu, Kaize Ding, Eric Wong:
Avoiding Copyright Infringement via Machine Unlearning. CoRR abs/2406.10952 (2024) - [i33]Adam Stein, Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong:
Towards Compositionality in Concept Learning. CoRR abs/2406.18534 (2024) - [i32]Anton Xue, Avishree Khare, Rajeev Alur, Surbhi Goel, Eric Wong:
Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference. CoRR abs/2407.00075 (2024) - [i31]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:
The FIX Benchmark: Extracting Features Interpretable to eXperts. CoRR abs/2409.13684 (2024) - [i30]Aaditya Naik, Jason Liu, Claire Wang, Saikat Dutta, Mayur Naik, Eric Wong:
Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning. CoRR abs/2410.03348 (2024) - [i29]Xiayan Ji, Anton Xue, Eric Wong, Oleg Sokolsky, Insup Lee:
AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties. CoRR abs/2410.24178 (2024) - 2023
- [c21]Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, Aleksander Madry:
A Data-Based Perspective on Transfer Learning. CVPR 2023: 3613-3622 - [c20]Shreya Havaldar, Matthew Pressimone, Eric Wong, Lyle H. Ungar:
Comparing Styles across Languages. EMNLP 2023: 6775-6791 - [c19]Shreya Havaldar, Adam Stein, Eric Wong, Lyle H. Ungar:
TopEx: Topic-based Explanations for Model Comparison. Tiny Papers @ ICLR 2023 - [c18]Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong:
Do Machine Learning Models Learn Statistical Rules Inferred from Data? ICML 2023: 25677-25693 - [c17]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 - [c16]Anton Xue, Rajeev Alur, Eric Wong:
Stability Guarantees for Feature Attributions with Multiplicative Smoothing. NeurIPS 2023 - [c15]Rahul Mysore Venkatesh, Eric Wong, Zico Kolter:
Adversarial robustness in discontinuous spaces via alternating sampling & descent. WACV 2023: 4651-4660 - [i28]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) - [i27]Natalie Maus, Patrick Chao, Eric Wong, Jacob R. Gardner:
Adversarial Prompting for Black Box Foundation Models. CoRR abs/2302.04237 (2023) - [i26]Tai Nguyen, Eric Wong:
In-context Example Selection with Influences. CoRR abs/2302.11042 (2023) - [i25]Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong:
Do Machine Learning Models Learn Common Sense? CoRR abs/2303.01433 (2023) - [i24]Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik:
Rectifying Group Irregularities in Explanations for Distribution Shift. CoRR abs/2305.16308 (2023) - [i23]Shreya Havaldar, Adam Stein, Eric Wong, Lyle H. Ungar:
TopEx: Topic-based Explanations for Model Comparison. CoRR abs/2306.00976 (2023) - [i22]Anton Xue, Rajeev Alur, Eric Wong:
Stability Guarantees for Feature Attributions with Multiplicative Smoothing. CoRR abs/2307.05902 (2023) - [i21]Aaditya Naik, Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik:
MDB: Interactively Querying Datasets and Models. CoRR abs/2308.06686 (2023) - [i20]Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas:
SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks. CoRR abs/2310.03684 (2023) - [i19]Shreya Havaldar, Matthew Pressimone, Eric Wong, Lyle H. Ungar:
Comparing Styles across Languages. CoRR abs/2310.07135 (2023) - [i18]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) - [i17]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) - [i16]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) - [i15]Andong Hua, Jindong Gu, Zhiyu Xue, Nicholas Carlini, Eric Wong, Yao Qin:
Initialization Matters for Adversarial Transfer Learning. CoRR abs/2312.05716 (2023) - 2022
- [c14]Hadi Salman, Saachi Jain, Eric Wong, Aleksander Madry:
Certified Patch Robustness via Smoothed Vision Transformers. CVPR 2022: 15116-15126 - [c13]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 - [c12]Eric Wong, J. Zico Kolter:
Learning perturbation sets for robust machine learning. ICLR 2021 - [c11]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
- [c10]Dean Dijour, Aadya Krishnaprasad, Ian Shei, Eric Wong:
Haptic Pattern Exploration in an Arm-Mounted Solenoid Array. HCI (38) 2020: 213-222 - [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
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