ReabsNet: Detecting and Revising Adversarial Examples. - Jiefeng Chen 001; Zihang Meng; Changtian Sun; Wei Tang; Yinglun Zhu (CoRR, 2017)

Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training. - Xi Wu 001; Uyeong Jang; Jiefeng Chen 001; Lingjiao Chen; Somesh Jha (ICML, 2018)

Improving Adversarial Robustness by Data-Specific Discretization. - Jiefeng Chen 001; Xi Wu 001; Yingyu Liang; Somesh Jha (CoRR, 2018)

Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks. - Jiefeng Chen 001; Xi Wu 001; Vaibhav Rastogi; Yingyu Liang; Somesh Jha (EuroS&P, 2019)

Robust Attribution Regularization. - Jiefeng Chen 001; Xi Wu 001; Vaibhav Rastogi; Yingyu Liang; Somesh Jha (NeurIPS, 2019)

Concise Explanations of Neural Networks using Adversarial Training. - Prasad Chalasani; Jiefeng Chen 001; Amrita Roy Chowdhury 001; Xi Wu 001; Somesh Jha (ICML, 2020)

AI-GAN: Attack-Inspired Generation of Adversarial Examples. - Tao Bai; Jun Zhao 007; Jinlin Zhu; Shoudong Han; Jiefeng Chen 001; Bo Li 026 (CoRR, 2020)

Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification. - Ryan Feng; Jiefeng Chen 001; Nelson R. Manohar; Earlence Fernandes; Somesh Jha; Atul Prakash 001 (CoRR, 2020)

Robust Out-of-distribution Detection in Neural Networks. - Jiefeng Chen 001; Yixuan Li 001; Xi Wu 001; Yingyu Liang; Somesh Jha (CoRR, 2020)

Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation. - Xi Wu 001; Yang Guo; Jiefeng Chen 001; Yingyu Liang; Somesh Jha; Prasad Chalasani (CoRR, 2020)

Robust Out-of-distribution Detection via Informative Outlier Mining. - Jiefeng Chen 001; Yixuan Li 001; Xi Wu 001; Yingyu Liang; Somesh Jha (CoRR, 2020)

AI-GAN: Attack-Inspired Generation of Adversarial Examples. - Tao Bai; Jun Zhao 007; Jinlin Zhu; Shoudong Han; Jiefeng Chen 001; Bo Li 026; Alex C. Kot (ICIP, 2021)

Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles. - Jiefeng Chen 001; Frederick Liu; Besim Avci; Xi Wu 001; Yingyu Liang; Somesh Jha (NeurIPS, 2021)

ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining. - Jiefeng Chen 001; Yixuan Li 001; Xi Wu 001; Yingyu Liang; Somesh Jha (ECML/PKDD (3), 2021)

Towards Adversarial Robustness via Transductive Learning. - Jiefeng Chen 001; Yang Guo; Xi Wu 001; Tianqi Li; Qicheng Lao; Yingyu Liang; Somesh Jha (CoRR, 2021)

Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method. - Tao Bai; Jun Zhao 007; Jinlin Zhu; Shoudong Han; Jiefeng Chen 001; Bo Li 026; Alex C. Kot (CoRR, 2021)

GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems. - Ryan Feng; Neal Mangaokar; Jiefeng Chen 001; Earlence Fernandes; Somesh Jha; Atul Prakash 001 (EuroS&P, 2022)

Towards Evaluating the Robustness of Neural Networks Learned by Transduction. - Jiefeng Chen 001; Xi Wu 001; Yang Guo; Yingyu Liang; Somesh Jha (ICLR, 2022)

Toward Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-Based Method. - Tao Bai; Jun Zhao 007; Jinlin Zhu; Shoudong Han; Jiefeng Chen 001; Bo Li 026; Alex ChiChung Kot (IEEE Internet Things J., 2022)

Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs. - Jiefeng Chen 001; Jinsung Yoon; Sayna Ebrahimi; Sercan Ö. Arik; Tomas Pfister; Somesh Jha (EMNLP (Findings), 2023)

Is Forgetting Less a Good Inductive Bias for Forward Transfer? - Jiefeng Chen 001; Timothy Nguyen; Dilan Görür; Arslan Chaudhry (ICLR, 2023)

The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning. - Zhenmei Shi; Jiefeng Chen 001; Kunyang Li 001; Jayaram Raghuram; Xi Wu 001; Yingyu Liang; Somesh Jha (ICLR, 2023)

Stratified Adversarial Robustness with Rejection. - Jiefeng Chen 001; Jayaram Raghuram; Jihye Choi; Xi Wu 001; Yingyu Liang; Somesh Jha (ICML, 2023)

Concept-based Explanations for Out-of-Distribution Detectors. - Jihye Choi; Jayaram Raghuram; Ryan Feng; Jiefeng Chen 001; Somesh Jha; Atul Prakash 001 (ICML, 2023)

Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection. - Nils Palumbo; Yang Guo; Xi Wu 001; Jiefeng Chen 001; Yingyu Liang; Somesh Jha (CoRR, 2023)

Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection. - Nils Palumbo; Yang Guo; Xi Wu 001; Jiefeng Chen 001; Yingyu Liang; Somesh Jha (ICML, 2024)

ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. - Jiefeng Chen 001; Jinsung Yoon; Sayna Ebrahimi; Sercan Ö. Arik; Somesh Jha; Tomas Pfister (Trans. Mach. Learn. Res., 2024)

Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models. - Fei Wang; Xingchen Wan; Ruoxi Sun 002; Jiefeng Chen 001; Sercan Ö. Arik (ACL (1), 2025)

SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling. - Jiefeng Chen 001; Jie Ren; Xinyun Chen; Chengrun Yang; Ruoxi Sun 002; Sercan Ö. Arik (CoRR, 2025)

MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement. - Jaehyun Nam; Jinsung Yoon; Jiefeng Chen 001; Jinwoo Shin; Sercan Ö. Arik; Tomas Pfister (CoRR, 2025)

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling. - Fei Wang; Xingchen Wan; Ruoxi Sun 002; Jiefeng Chen 001; Sercan Ö. Arik (CoRR, 2025)