Eric Wong - RankMe

100 papers A* 29A 1C 1Journal 60Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CAMEL: An ECG Language Model for Forecasting Cardiac Events. CoRR 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
2026	J	`jnl`	Missingness Bias Calibration in Feature Attribution Explanations. CoRR Shailesh Sridhar, Anton Xue, Eric Wong
2026	J	`jnl`	Semantics-Preserving Evasion of LLM Vulnerability Detectors. CoRR Luze Sun, Alina Oprea, Eric Wong
2025	J	`jnl`	Adaptively evaluating models with task elicitation. CoRR Davis Brown, Prithvi Balehannina, Helen Jin, Shreya Havaldar, Hamed Hassani, Eric Wong
2025	A*	`conf`	Adaptively profiling models with task elicitation. EMNLP Davis Brown, Prithvi Balehannina, Helen Jin, Shreya Havaldar, Hamed Hassani, Eric Wong
2025	—	`conf`	Avoiding Copyright Infringement via Large Language Model Unlearning. NAACL (Findings) Guangyao Dou, Zheyuan Liu, Qing Lyu, Kaize Ding, Eric Wong
2025	J	`jnl`	Benchmarking Misuse Mitigation Against Covert Adversaries. CoRR Davis Brown, Mahdi Sabbaghi, Luze Sun, Alexander Robey, George J. Pappas, Eric Wong, Hamed Hassani
2025	J	`jnl`	BrowserArena: Evaluating LLM Agents on Real-World Web Navigation Tasks. CoRR Sagnik Anupam, Davis Brown, Shuo Li, Eric Wong, Hamed Hassani, Osbert Bastani
2025	J	`jnl`	CTSketch: Compositional Tensor Sketching for Scalable Neurosymbolic Learning. CoRR Seewon Choi, Alaia Solko-Breslin, Rajeev Alur, Eric Wong
2025	A*	`conf`	DOLPHIN: A Programmable Framework for Scalable Neurosymbolic Learning. ICML Aaditya Naik, Jason Liu, Claire Wang, Amish Sethi, Saikat Dutta, Mayur Naik, Eric Wong
2025	—	`conf`	Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing. IJCNLP-AACL (Short Papers) Jiabao Ji, Bairu Hou, Alexander Robey, George J. Pappas, Hamed Hassani, Yang Zhang, Eric Wong, Shiyu Chang
2025	A*	`conf`	Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs. EMNLP Andong Hua, Kenan Tang, Chenhe Gu, Jindong Gu, Eric Wong, Yao Qin
2025	J	`jnl`	Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs. CoRR Andong Hua, Kenan Tang, Chenhe Gu, Jindong Gu, Eric Wong, Yao Qin
2025	J	`jnl`	Instruction Following by Boosting Attention of Large Language Models. CoRR Vitoria Guardieiro, Adam Stein, Avishree Khare, Eric Wong
2025	—	`conf`	Jailbreaking Black Box Large Language Models in Twenty Queries. SaTML Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong
2025	A*	`conf`	Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference. ICLR Anton Xue, Avishree Khare, Rajeev Alur, Surbhi Goel, Eric Wong
2025	J	`jnl`	NSF-SciFy: Mining the NSF Awards Database for Scientific Claims. CoRR Delip Rao, Weiqiu You, Eric Wong, Chris Callison-Burch
2025	J	`jnl`	Once Upon an Input: Reasoning via Per-Instance Program Synthesis. CoRR Adam Stein, Neelay Velingker, Mayur Naik, Eric Wong
2025	A*	`conf`	Probabilistic Soundness Guarantees in LLM Reasoning Chains. EMNLP Weiqiu You, Anton Xue, Shreya Havaldar, Delip Rao, Helen Jin, Chris Callison-Burch, Eric Wong
2025	J	`jnl`	Probabilistic Soundness Guarantees in LLM Reasoning Chains. CoRR Weiqiu You, Anton Xue, Shreya Havaldar, Delip Rao, Helen Jin, Chris Callison-Burch, Eric Wong
2025	J	`jnl`	Probabilistic Stability Guarantees for Feature Attributions. CoRR Helen Jin, Anton Xue, Weiqiu You, Surbhi Goel, Eric Wong
2025	J	`jnl`	SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks. Trans. Mach. Learn. Res. Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas
2025	J	`jnl`	Stable Prediction of Adverse Events in Medical Time-Series Data. CoRR Mayank Keoliya, Seewon Choi, Rajeev Alur, Mayur Naik, Eric Wong
2025	A*	`conf`	Sum-of-Parts: Self-Attributing Neural Networks with End-to-End Learning of Feature Groups. ICML Weiqiu You, Helen Qu, Marco Gatti, Bhuvnesh Jain, Eric Wong
2025	J	`jnl`	SuperActivators: Only the Tail of the Distribution Contains Reliable Concept Signals. CoRR Cassandra Goldberg, Chaehyeon Kim, Adam Stein, Eric Wong
2025	J	`jnl`	T-FIX: Text-Based Explanations with Features Interpretable to eXperts. CoRR 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
2025	J	`jnl`	The FIX Benchmark: Extracting Features Interpretable to eXperts. J. Data-centric Mach. Learn. Res. 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
2025	J	`jnl`	The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models. CoRR Adam Stein, Aaditya Naik, Neelay Velingker, Mayur Naik, Eric Wong
2025	—	`conf`	Towards Style Alignment in Cross-Cultural Translation. ACL (1) Shreya Havaldar, Adam Stein, Eric Wong, Lyle H. Ungar
2025	J	`jnl`	Towards Style Alignment in Cross-Cultural Translation. CoRR Shreya Havaldar, Adam Stein, Eric Wong, Lyle H. Ungar
2025	J	`jnl`	Where's the Bug? Attention Probing for Scalable Fault Localization. CoRR Adam Stein, Arthur Wayne, Aaditya Naik, Mayur Naik, Eric Wong
2024	A*	`conf`	AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties. NeurIPS Xiayan Ji, Anton Xue, Eric Wong, Oleg Sokolsky, Insup Lee
2024	J	`jnl`	AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties. CoRR Xiayan Ji, Anton Xue, Eric Wong, Oleg Sokolsky, Insup Lee
2024	J	`jnl`	Avoiding Copyright Infringement via Machine Unlearning. CoRR Guangyao Dou, Zheyuan Liu, Qing Lyu, Kaize Ding, Eric Wong
2024	A*	`conf`	DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation. ICML Yinjun Wu, Mayank Keoliya, Kan Chen, Neelay Velingker, Ziyang Li, Emily J. Getzen, Qi Long, Mayur Naik, Ravi B. Parikh, Eric Wong
2024	J	`jnl`	DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation. CoRR Yinjun Wu, Mayank Keoliya, Kan Chen, Neelay Velingker, Ziyang Li, Emily J. Getzen, Qi Long, Mayur Naik, Ravi B. Parikh, Eric Wong
2024	A*	`conf`	Data-Efficient Learning with Neural Programs. NeurIPS Alaia Solko-Breslin, Seewon Choi, Ziyang Li, Neelay Velingker, Rajeev Alur, Mayur Naik, Eric Wong
2024	J	`jnl`	Data-Efficient Learning with Neural Programs. CoRR Alaia Solko-Breslin, Seewon Choi, Ziyang Li, Neelay Velingker, Rajeev Alur, Mayur Naik, Eric Wong
2024	J	`jnl`	Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing. CoRR Jiabao Ji, Bairu Hou, Alexander Robey, George J. Pappas, Hamed Hassani, Yang Zhang, Eric Wong, Shiyu Chang
2024	J	`jnl`	Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning. CoRR Aaditya Naik, Jason Liu, Claire Wang, Saikat Dutta, Mayur Naik, Eric Wong
2024	—	`conf`	Evaluating Groups of Features via Consistency, Contiguity, and Stability. Tiny Papers @ ICLR Chaehyeon Kim, Weiqiu You, Shreya Havaldar, Eric Wong
2024	A*	`conf`	Initialization Matters for Adversarial Transfer Learning. CVPR Andong Hua, Jindong Gu, Zhiyu Xue, Nicholas Carlini, Eric Wong, Yao Qin
2024	A*	`conf`	JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. NeurIPS 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
2024	J	`jnl`	JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. CoRR 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
2024	J	`jnl`	Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference. CoRR Anton Xue, Avishree Khare, Rajeev Alur, Surbhi Goel, Eric Wong
2024	A*	`conf`	SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation. ICLR Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, Sijia Liu
2024	J	`jnl`	The FIX Benchmark: Extracting Features Interpretable to eXperts. CoRR 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
2024	J	`jnl`	TorchQL: A Programming Framework for Integrity Constraints in Machine Learning. Proc. ACM Program. Lang. Aaditya Naik, Adam Stein, Yinjun Wu, Mayur Naik, Eric Wong
2024	A*	`conf`	Towards Compositionality in Concept Learning. ICML Adam Stein, Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong
2024	J	`jnl`	Towards Compositionality in Concept Learning. CoRR Adam Stein, Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong
2023	A*	`conf`	A Data-Based Perspective on Transfer Learning. CVPR Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, Aleksander Madry
2023	J	`jnl`	Adversarial Prompting for Black Box Foundation Models. CoRR Natalie Maus, Patrick Chao, Eric Wong, Jacob R. Gardner
2023	A	`conf`	Adversarial robustness in discontinuous spaces via alternating sampling & descent. WACV Rahul Mysore Venkatesh, Eric Wong, Zico Kolter
2023	A*	`conf`	Comparing Styles across Languages. EMNLP Shreya Havaldar, Matthew Pressimone, Eric Wong, Lyle H. Ungar
2023	J	`jnl`	Comparing Styles across Languages. CoRR Shreya Havaldar, Matthew Pressimone, Eric Wong, Lyle H. Ungar
2023	J	`jnl`	Do Machine Learning Models Learn Common Sense? CoRR Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong
2023	A*	`conf`	Do Machine Learning Models Learn Statistical Rules Inferred from Data? ICML Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong
2023	—	`conf`	Faithful Chain-of-Thought Reasoning. IJCNLP (1) Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, Chris Callison-Burch
2023	J	`jnl`	Faithful Chain-of-Thought Reasoning. CoRR Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, Chris Callison-Burch
2023	J	`jnl`	In-context Example Selection with Influences. CoRR Tai Nguyen, Eric Wong
2023	J	`jnl`	Initialization Matters for Adversarial Transfer Learning. CoRR Andong Hua, Jindong Gu, Zhiyu Xue, Nicholas Carlini, Eric Wong, Yao Qin
2023	J	`jnl`	Jailbreaking Black Box Large Language Models in Twenty Queries. CoRR Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong
2023	J	`jnl`	MDB: Interactively Querying Datasets and Models. CoRR Aaditya Naik, Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik
2023	J	`jnl`	Rectifying Group Irregularities in Explanations for Distribution Shift. CoRR Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik
2023	J	`jnl`	SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation. CoRR Chongyu Fan, Jiancheng Liu, Yihua Zhang, Dennis Wei, Eric Wong, Sijia Liu
2023	J	`jnl`	SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks. CoRR Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas
2023	A*	`conf`	Stability Guarantees for Feature Attributions with Multiplicative Smoothing. NeurIPS Anton Xue, Rajeev Alur, Eric Wong
2023	J	`jnl`	Stability Guarantees for Feature Attributions with Multiplicative Smoothing. CoRR Anton Xue, Rajeev Alur, Eric Wong
2023	J	`jnl`	Sum-of-Parts Models: Faithful Attributions for Groups of Features. CoRR Weiqiu You, Helen Qu, Marco Gatti, Bhuvnesh Jain, Eric Wong
2023	—	`conf`	TopEx: Topic-based Explanations for Model Comparison. Tiny Papers @ ICLR Shreya Havaldar, Adam Stein, Eric Wong, Lyle H. Ungar
2023	J	`jnl`	TopEx: Topic-based Explanations for Model Comparison. CoRR Shreya Havaldar, Adam Stein, Eric Wong, Lyle H. Ungar
2022	J	`jnl`	A Data-Based Perspective on Transfer Learning. CoRR Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, Aleksander Madry
2022	A*	`conf`	Certified Patch Robustness via Smoothed Vision Transformers. CVPR Hadi Salman, Saachi Jain, Eric Wong, Aleksander Madry
2022	A*	`conf`	Missingness Bias in Model Debugging. ICLR Saachi Jain, Hadi Salman, Eric Wong, Pengchuan Zhang, Vibhav Vineet, Sai Vemprala, Aleksander Madry
2022	J	`jnl`	Missingness Bias in Model Debugging. CoRR Saachi Jain, Hadi Salman, Eric Wong, Pengchuan Zhang, Vibhav Vineet, Sai Vemprala, Aleksander Madry
2022	J	`jnl`	When does Bias Transfer in Transfer Learning? CoRR Hadi Salman, Saachi Jain, Andrew Ilyas, Logan Engstrom, Eric Wong, Aleksander Madry
2021	J	`jnl`	Certified Patch Robustness via Smoothed Vision Transformers. CoRR Hadi Salman, Saachi Jain, Eric Wong, Aleksander Madry
2021	J	`jnl`	DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting. CoRR Shaoru Chen, Eric Wong, J. Zico Kolter, Mahyar Fazlyab
2021	A*	`conf`	Learning perturbation sets for robust machine learning. ICLR Eric Wong, J. Zico Kolter
2021	A*	`conf`	Leveraging Sparse Linear Layers for Debuggable Deep Networks. ICML Eric Wong, Shibani Santurkar, Aleksander Madry
2021	J	`jnl`	Leveraging Sparse Linear Layers for Debuggable Deep Networks. CoRR Eric Wong, Shibani Santurkar, Aleksander Madry
2021	—	`—`	Provable, Structured, and Efficient Methods for Robustness of Deep Networks to Adversarial Examples. Eric Wong
2020	A*	`conf`	Adversarial Robustness Against the Union of Multiple Perturbation Models. ICML Pratyush Maini, Eric Wong, J. Zico Kolter
2020	A*	`conf`	Fast is better than free: Revisiting adversarial training. ICLR Eric Wong, Leslie Rice, J. Zico Kolter
2020	J	`jnl`	Fast is better than free: Revisiting adversarial training. CoRR Eric Wong, Leslie Rice, J. Zico Kolter
2020	—	`conf`	Haptic Pattern Exploration in an Arm-Mounted Solenoid Array. HCI (38) Dean Dijour, Aadya Krishnaprasad, Ian Shei, Eric Wong
2020	J	`jnl`	Learning perturbation sets for robust machine learning. CoRR Eric Wong, J. Zico Kolter
2020	C	`conf`	Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications. IV Eric Wong, Tim Schneider, Joerg Schmitt, Frank R. Schmidt, J. Zico Kolter
2020	J	`jnl`	Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications. CoRR Eric Wong, Tim Schneider, Joerg Schmitt, Frank R. Schmidt, J. Zico Kolter
2020	A*	`conf`	Overfitting in adversarially robust deep learning. ICML Leslie Rice, Eric Wong, J. Zico Kolter
2020	J	`jnl`	Overfitting in adversarially robust deep learning. CoRR Leslie Rice, Eric Wong, J. Zico Kolter
2019	J	`jnl`	Adversarial Robustness Against the Union of Multiple Perturbation Models. CoRR Pratyush Maini, Eric Wong, J. Zico Kolter
2019	A*	`conf`	Wasserstein Adversarial Examples via Projected Sinkhorn Iterations. ICML Eric Wong, Frank R. Schmidt, J. Zico Kolter
2019	J	`jnl`	Wasserstein Adversarial Examples via Projected Sinkhorn Iterations. CoRR Eric Wong, Frank R. Schmidt, J. Zico Kolter
2018	A*	`conf`	Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope. ICML Eric Wong, J. Zico Kolter
2018	A*	`conf`	Scaling provable adversarial defenses. NeurIPS Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen, J. Zico Kolter
2018	J	`jnl`	Scaling provable adversarial defenses. CoRR Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen, J. Zico Kolter
2017	A*	`conf`	A Semismooth Newton Method for Fast, Generic Convex Programming. ICML Alnur Ali, Eric Wong, J. Zico Kolter
2017	J	`jnl`	Provable defenses against adversarial examples via the convex outer adversarial polytope. CoRR J. Zico Kolter, Eric Wong
2015	A*	`conf`	An SVD and Derivative Kernel Approach to Learning from Geometric Data. AAAI Eric Wong, J. Zico Kolter