I am an Assistant Professor in the College of Information Sciences and Technology at Penn State University. I received my Ph.D. in the Department of Computer Science, University of California, Los Angeles (UCLA) working with Prof. Quanquan Gu in 2021. I received my B.E. in the Department of Electrical Engineering and Information Science at the University of Science and Technology of China in 2015.
My research interests broadly include the theory and applications in different aspects of machine learning (machine learning robustness, machine learning efficiency, adversarial machine learning, etc.)
Openings: I’m looking for highly motivated students (including PhDs, Masters, undergraduates), and interns to join my group. If you’re interested in joining my lab, please fill and see instructions in the following form (feel free to skip optional questions).
Full publications on Google Scholar.
E indicates authors with equal contribution. # indicates students supervised.
Do Imperceptible Perturbations Really Prevent Unauthorized Data Usage in Diffusion-based Image Generation Systems?
Bochuan Cao#, Changjiang Li, Ting Wang, Jinyuan Jia, Bo Li and Jinghui Chen
in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023.
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
Ziyi Yin, Muchao Ye, Tianrong Zhang#, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang and Fenglong Ma
in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023.
A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning
Hangfan Zhang, Jinyuan Jia, Jinghui Chen, Lu Lin and Dinghao Wu
in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023.
Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
Tianyu Du, Zhaohan Xi, Changjiang Li, Ren Pang, Shouling Ji, Jinghui Chen, Fenglong Ma and Ting Wang
in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023.
UniT: A Unified Look at Certified Robust Training against Text Adversarial Perturbation
Muchao Ye, Ziyi Yin, Tianrong Zhang#, Tianyu Du, Jinghui Chen, Ting Wang and Fenglong Ma
in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023.
RoCourseNet: Robust Training of a Prediction Aware Recourse Model
Hangzhi Guo, Feiran Jia, Jinghui Chen, Anna Squicciarini and Amulya Yadav
in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM), Birmingham, UK, 2023.
A short version of this paper also appears on ICML 2022 Workshop on Adversarial Machine Learning Frontiers.
PAT: Geometry-Aware Hard-Label Black-Box Adversarial Attacks on Text
Muchao Ye, Jinghui Chen, Chenglin Miao, Han Liu, Ting Wang and Fenglong Ma
in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), San Diego, CA, USA, 2023.
Benign Overfitting in Adversarially Robust Linear Classification
Jinghui ChenE, Yuan CaoE, and Quanquan Gu
in Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), Pittsburgh, PA, USA, 2023.
Graph Contrastive Backdoor Attacks
Hangfan Zhang, Jinghui Chen, Lu Lin, Jinyuan Jia and Dinghao Wu
in Proceedings of the 40th International Conference on Machine Learning (ICML), Hawaii, USA, 2023.
Multiple Models for Outbreak Decision Support in the Face of Uncertainty
Katriona Shea, ..., Jinghui Chen, ..., Michael C. Runge.
in Proceedings of the National Academy of Sciences (PNAS), 2023.
Do Language Models Plagiarize?
Lee, Jooyoung, Thai Le, Jinghui Chen, and Dongwon Lee
in Proceedings of the ACM Web Conference (WWW), Austin, Texas, USA, 2023
Spectral Augmentation for Self-Supervised Learning on Graphs
Lu Lin, Jinghui Chen, Hongning Wang
in Proceedings of the 11th International Conference on Learning Representations (ICLR), Kigali Rwanda, 2023.
A short version of this paper also appears on NeurIPS 2022 Workshop on New Frontiers in Graph Learning.
On the Vulnerability of Backdoor Defenses for Federated Learning
Pei Fang# and Jinghui Chen
in Proceedings of the 37th Conference on Artificial Intelligence (AAAI), Washington DC, USA, 2023.
A short version of this paper also appears on International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022.
One-shot Neural Backdoor Erasing via Adversarial Weight Masking
Shuwen Chai# and Jinghui Chen
in Proceedings of the 36th Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, 2022.
How Powerful is Implicit Denoising in Graph Neural Networks
Songtao Liu, Zhitao Ying, Hanze Dong, Lu Lin, Jinghui Chen and Dinghao Wu
NeurIPS 2022 Workshop on New Frontiers in Graph Learning.
The United States COVID-19 Forecast Hub dataset
Estee Y Cramer, ..., Jinghui Chen, ..., Nicholas G. Reich
Scientific Data, 9(1), pp.1-15., 2022.
LeapAttack: Hard-Label Adversarial Attack on Text via Gradient-Based Optimization
Muchao Ye, Jinghui Chen, Chenglin Miao, Ting Wang and Fenglong Ma
in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Washington DC, USA, 2022.
Communication-Efficient Adaptive Federated Learning
Yujia Wang# , Lu Lin and Jinghui Chen
in Proceedings of the 39th International Conference on Machine Learning (ICML), Baltimore, Maryland, USA, 2022.
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US
Estee Y Cramer, ..., Jinghui Chen, ..., Nicholas G. Reich
in Proceedings of the National Academy of Sciences (PNAS), 2022.
Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations
Weiqi Peng# and Jinghui Chen
in Proceedings of the 10th International Conference on Learning Representations (ICLR), Virtual, 2022.
Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization
Yujia Wang# , Lu Lin and Jinghui Chen
in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), Virtual, 2022.
A short version of this paper also appears on International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022 (FL-AAAI-22).
Efficient Robust Training via Backward Smoothing
Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu and Jingjing Liu
In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI), Vancouver, BC, Canada, 2022.
Do Wider Neural Networks Really Help Adversarial Robustness?
Boxi WuE, Jinghui ChenE, Deng Cai, Xiaofei He and Quanquan Gu
in Proceedings of the 35th Advances in Neural Information Processing Systems (NeurIPS), Virtual, 2021.
Benign Overfitting in Adversarially Robust Linear Classification
Jinghui ChenE, Yuan CaoE and Quanquan Gu
ICML 2021 Workshop on Overparameterization: Pitfalls and Opportunities.
Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States
Difan Zou, Lingxiao Wang, Pan Xu, Jinghui Chen, Weitong Zhang and Quanquan Gu
ICLR 2021 Workshop on Machine Learning for Preventingand Combating Pandemics.
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
Dongruo ZhouE, Jinghui ChenE, Yuan CaoE, Yiqi Tang, Ziyan Yang, and Quanquan Gu
NeurIPS 2020 Workshop on Optimization for Machine Learning.
Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S
COVID-19 Forecast Hub Consortium, Jinghui Chen.
medRxiv:2020.08.19.20177493, 2020.
RayS: A Ray Searching Method for Hard-label Adversarial Attack
Jinghui Chen and Quanquan Gu
in Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), San Diego, CA, USA 2020.
A short version of this paper also appears on ICML 2020 Workshop on Uncertainty & Robustness in Deep Learning and ECCV 2020 Workshop on Adversarial Robustness in the Real World.
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao and Quanquan Gu
in Proceedings of 29th International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan, 2020.
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
Xiao ZhangE, Jinghui ChenE, Quanquan Gu and David Evans
in Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Sicily, Italy, 2020.
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
Jinghui Chen, Dongruo Zhou, Jinfeng Yi and Quanquan Gu
in Proceedings of the 34th Conference on Artificial Intelligence (AAAI), New York, New York, USA, 2020.
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
Pan XuE, Jinghui ChenE, Difan Zou and Quanquan Gu
in Proceedings of the 32nd Advances in Neural Information Processing Systems (NIPS), Montréal, Canada, 2018.
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma and Quanquan Gu
in Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.
Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization
Jinghui Chen and Quanquan Gu
in Proceedings of the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Halifax, Nova Scotia, Canada, 2017.
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption
Jinghui Chen, Lingxiao Wang, Xiao Zhang, and Quanquan Gu
arXiv:1704.06256, 2017.
Outlier Detection with Autoencoder Ensembles
Jinghui Chen, Saket Sathe, Charu Aggarwal, and Deepak Turaga
in Proceedings of the 17th SIAM International Conference on Data Mining (SDM), Houston, Texas, USA, 2017.
Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Optimization
Jinghui Chen and Quanquan Gu
in Proceedings of the 32th International Conference on Uncertainty in Artificial Intelligence (UAI), New York, USA, 2016.
Improved threshold Selection by using Calibrated Probabilities for Random Forest Classifiers
Florian Baumann, Jinghui Chen, Karsten Vogt and Bodo Rosenhahn
in Proceedings of the 12th Conference on Computer and Robot Vision (CRV), Halifax, Nova Scotia, Canada, 2015.
Do Imperceptible Perturbations Really Prevent Unauthorized Data Usage in Diffusion-based Image Generation Systems?
Bochuan Cao#, Changjiang Li, Ting Wang, Jinyuan Jia, Bo Li and Jinghui Chen
in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023.
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
Ziyi Yin, Muchao Ye, Tianrong Zhang#, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang and Fenglong Ma
in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023.
A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning
Hangfan Zhang, Jinyuan Jia, Jinghui Chen, Lu Lin and Dinghao Wu
in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023.
Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
Tianyu Du, Zhaohan Xi, Changjiang Li, Ren Pang, Shouling Ji, Jinghui Chen, Fenglong Ma and Ting Wang
in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023.
UniT: A Unified Look at Certified Robust Training against Text Adversarial Perturbation
Muchao Ye, Ziyi Yin, Tianrong Zhang#, Tianyu Du, Jinghui Chen, Ting Wang and Fenglong Ma
in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023.
RoCourseNet: Robust Training of a Prediction Aware Recourse Model
Hangzhi Guo, Feiran Jia, Jinghui Chen, Anna Squicciarini and Amulya Yadav
in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM), Birmingham, UK, 2023.
A short version of this paper also appears on ICML 2022 Workshop on Adversarial Machine Learning Frontiers.
PAT: Geometry-Aware Hard-Label Black-Box Adversarial Attacks on Text
Muchao Ye, Jinghui Chen, Chenglin Miao, Han Liu, Ting Wang and Fenglong Ma
in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), San Diego, CA, USA, 2023.
Benign Overfitting in Adversarially Robust Linear Classification
Jinghui ChenE, Yuan CaoE, and Quanquan Gu
in Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), Pittsburgh, PA, USA, 2023.
Graph Contrastive Backdoor Attacks
Hangfan Zhang, Jinghui Chen, Lu Lin, Jinyuan Jia and Dinghao Wu
in Proceedings of the 40th International Conference on Machine Learning (ICML), Hawaii, USA, 2023.
Do Language Models Plagiarize?
Lee, Jooyoung, Thai Le, Jinghui Chen, and Dongwon Lee
in Proceedings of the Web Conference (WWW), Austin, Texas, USA, 2023
Spectral Augmentation for Self-Supervised Learning on Graphs
Lu Lin, Jinghui Chen, Hongning Wang
in Proceedings of the 11th International Conference on Learning Representations (ICLR), Kigali Rwanda, 2023.
A short version of this paper also appears on NeurIPS 2022 Workshop on New Frontiers in Graph Learning.
On the Vulnerability of Backdoor Defenses for Federated Learning
Pei Fang# and Jinghui Chen
in Proceedings of the 37th Conference on Artificial Intelligence (AAAI), Washington DC, USA, 2023.
A short version of this paper also appears on International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022.
One-shot Neural Backdoor Erasing via Adversarial Weight Masking
Shuwen Chai# and Jinghui Chen
in Proceedings of the 36th Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, 2022.
How Powerful is Implicit Denoising in Graph Neural Networks
Songtao Liu, Zhitao Ying, Hanze Dong, Lu Lin, Jinghui Chen and Dinghao Wu
NeurIPS 2022 Workshop on New Frontiers in Graph Learning.
LeapAttack: Hard-Label Adversarial Attack on Text via Gradient-Based Optimization
Muchao Ye, Jinghui Chen, Chenglin Miao, Ting Wang and Fenglong Ma
in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Washington DC, USA, 2022.
Communication-Efficient Adaptive Federated Learning
Yujia Wang#, Lu Lin and Jinghui Chen
in Proceedings of the 39th International Conference on Machine Learning (ICML), Baltimore, Maryland, USA, 2022.
Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations
Weiqi Peng# and Jinghui Chen
in Proceedings of the 10th International Conference on Learning Representations (ICLR), Virtual, 2022.
Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization
Yujia Wang#, Lu Lin and Jinghui Chen
in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), Virtual, 2022.
A short version of this paper also appears on International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022.
Efficient Robust Training via Backward Smoothing
Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu and Jingjing Liu
in Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI), Vancouver, BC, Canada, 2022.
Do Wider Neural Networks Really Help Adversarial Robustness?
Boxi WuE, Jinghui ChenE, Deng Cai, Xiaofei He and Quanquan Gu
in Proceedings of the 35th Advances in Neural Information Processing Systems (NeurIPS), Virtual, 2021.
Benign Overfitting in Adversarially Robust Linear Classification
Jinghui ChenE, Yuan CaoE and Quanquan Gu
ICML 2021 Workshop on Overparameterization: Pitfalls and Opportunities.
The research of our lab is focused on different aspects of machine learning (efficiency, robustness, interpretability, responsibility, trustworthiness) and their applications in computer vision, graph learning, anomaly detection, cybersecurity, recommendation systems, computational genomics, etc. Some of our current research projects are: