Jiefeng Chen

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 36

Top Topics

Number Of Labeled Examples

Top Venues

Publications

Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Somesh Jha, Tomas Pfister
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. Trans. Mach. Learn. Res. 2024 (2024)
Jiefeng Chen, Timothy Nguyen, Dilan Görür, Arslan Chaudhry
Is Forgetting Less a Good Inductive Bias for Forward Transfer? ICLR (2023)
Jiefeng Chen, Timothy Nguyen, Dilan Görür, Arslan Chaudhry
Is forgetting less a good inductive bias for forward transfer? CoRR (2023)
Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, Somesh Jha
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning. ICLR (2023)
Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Tomas Pfister, Somesh Jha
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs. CoRR (2023)
Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, Yingyu Liang, Somesh Jha
Stratified Adversarial Robustness with Rejection. CoRR (2023)
Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, Somesh Jha
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning. CoRR (2023)
Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Somesh Jha, Tomas Pfister
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. CoRR (2023)
Jihye Choi, Jayaram Raghuram, Ryan Feng, Jiefeng Chen, Somesh Jha, Atul Prakash
Concept-based Explanations for Out-of-Distribution Detectors. ICML (2023)
Nils Palumbo, Yang Guo, Xi Wu, Jiefeng Chen, Yingyu Liang, Somesh Jha
Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection. CoRR (2023)
Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, Yingyu Liang, Somesh Jha
Stratified Adversarial Robustness with Rejection. ICML (2023)
Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö Arik, Tomas Pfister, Somesh Jha
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs. EMNLP (Findings) (2023)
Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, Alex ChiChung Kot
Toward Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-Based Method. IEEE Internet Things J. 9 (3) (2022)
Jihye Choi, Jayaram Raghuram, Ryan Feng, Jiefeng Chen, Somesh Jha, Atul Prakash
Concept-based Explanations for Out-Of-Distribution Detectors. CoRR (2022)
Ryan Feng, Neal Mangaokar, Jiefeng Chen, Earlence Fernandes, Somesh Jha, Atul Prakash
GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems. EuroS&P (2022)
Jiefeng Chen, Xi Wu, Yang Guo, Yingyu Liang, Somesh Jha
Towards Evaluating the Robustness of Neural Networks Learned by Transduction. ICLR (2022)
Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, Alex C. Kot
Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method. CoRR (2021)
Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, Alex C. Kot
AI-GAN: Attack-Inspired Generation of Adversarial Examples. ICIP (2021)
Jiefeng Chen, Xi Wu, Yang Guo, Yingyu Liang, Somesh Jha
Towards Evaluating the Robustness of Neural Networks Learned by Transduction. CoRR (2021)
Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, Somesh Jha
Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles. CoRR (2021)
Jiefeng Chen, Yang Guo, Xi Wu, Tianqi Li, Qicheng Lao, Yingyu Liang, Somesh Jha
Towards Adversarial Robustness via Transductive Learning. CoRR (2021)
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha
ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining. ECML/PKDD (3) (2021)
Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, Somesh Jha
Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles. NeurIPS (2021)
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha
Robust Out-of-distribution Detection in Neural Networks. CoRR (2020)
Ryan Feng, Jiefeng Chen, Nelson R. Manohar, Earlence Fernandes, Somesh Jha, Atul Prakash
Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification. CoRR (2020)
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha
Robust Out-of-distribution Detection via Informative Outlier Mining. CoRR (2020)
Xi Wu, Yang Guo, Jiefeng Chen, Yingyu Liang, Somesh Jha, Prasad Chalasani
Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation. CoRR (2020)
Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li
AI-GAN: Attack-Inspired Generation of Adversarial Examples. CoRR (2020)
Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Xi Wu, Somesh Jha
Concise Explanations of Neural Networks using Adversarial Training. ICML (2020)
Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha
Robust Attribution Regularization. CoRR (2019)
Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha
Robust Attribution Regularization. NeurIPS (2019)
Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha
Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks. EuroS&P (2019)
Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Somesh Jha, Xi Wu
Concise Explanations of Neural Networks using Adversarial Training. CoRR (2018)
Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training. ICML (2018)
Jiefeng Chen, Xi Wu, Yingyu Liang, Somesh Jha
Improving Adversarial Robustness by Data-Specific Discretization. CoRR (2018)
Jiefeng Chen, Zihang Meng, Changtian Sun, Wei Tang, Yinglun Zhu
ReabsNet: Detecting and Revising Adversarial Examples. CoRR (2017)