Jinghui Chen
Assistant Professor, Ph.D.
Penn State University
Email: jzc5917 [at] psu [dot] edu
About
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).
News
- [09/2023] Five papers are accepted to NeurIPS 2023!
- [08/2023] We are organizing the Workshop on Federated Learning in the Age of Foundation Models at NeurIPS 2023 (FL@FM-NeurIPS’23). Submissions are welcome!
- [08/2023] Our paper is accepted to CIKM 2023: "RoCourseNet: Robust Training of a Prediction Aware Recourse Model"
- [05/2023] Our paper is accepted to KDD 2023: "PAT: Geometry-Aware Hard-Label Black-Box Adversarial Attacks on Text"
- [05/2023] Our paper is accepted to UAI 2023: "Benign Overfitting in Adversarially Robust Linear Classification"
- [04/2023] Our paper is accepted to ICML 2023: "Graph Contrastive Backdoor Attacks"
- [01/2023] Our paper is accepted to WWW 2023: "Do Language Models Plagiarize?"
- [01/2023] Our paper is accepted to ICLR 2023: "Spectral Augmentation for Self-Supervised Learning on Graphs"
- [12/2022] Our paper is accepted to AAAI 2023: "On the Vulnerability of Backdoor Defenses for Federated Learning"
- [09/2022] Our paper is accepted to NeurIPS 2022: "One-shot Neural Backdoor Erasing via Adversarial Weight Masking"
- [05/2022] Our paper is accepted to KDD 2022: "LeapAttack: Hard-Label Adversarial Attack on Text via Gradient-Based Optimization"
- [05/2022] Our paper is accepted to ICML 2022: "Communication-Efficient Adaptive Federated Learning"
- [05/2022] Dr. Chen recieved received Cisco Faculty Research Award!
- [01/2022] Our paper is accepted to ICLR 2022: "Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations"
- [01/2022] Our paper is accepted to AISTATS 2022: "Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization"
- [12/2021] Our paper is accepted to AAAI 2022: "Efficient Robust Training via Backward Smoothing"
- [09/2021] Our paper is accepted to NeurIPS 2021: "Do Wider Neural Networks Really Help Adversarial Robustness?"
- [06/2021] I recieved UCLA Outstanding Graduate Student Research Award.
- [04/2021] I will join the College of Information Sciences and Technology (IST) at Penn State University (PSU) in Fall 2021 as a tenure-track assistant professor.
- [07/2020] Released Model Robustness (ADBD) Leaderboard under RayS attack: Benchmarking state-of-the-art robust trained models with ADBD metric
- [05/2020] Our paper is accepted to KDD 2020: "RayS: A Ray Searching Method for Hard-label Adversarial Attack"
- [04/2020] Our paper is accepted to IJCAI 2020: "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks"
- [04/2020] We just launched a project using machine learning and AI to combat Covid-19! Live data visualization and new cases / peak predictions
- [01/2020] Our paper is accepted to AISTATS 2020: "Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models"
- [11/2019] Our paper is accepted to AAAI 2020: "A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks"
Publications
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.
Research
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:
Studying the vulnerabilities inside the current LLMs and how we can improve them for better trustworthiness.
Studying the convergence of machine learning optimizers including adaptive gradient optimizers and designing new generation of optimizers for deep learning.
Studying the effects of poisoning and backdoor attacks on deep learning models, as well as how to mitigate those threats.
Addressing the emerging challenges for Federated Learning in practical scenarios such as data and model heterogeneity, communication efficiency, as well as security and privacy issues.
Evaluating, understanding, and improving adversarial robustness in deep learning as well as studying the theoretical foundations behind adversarial training and robust learning.
Improving the robustness of the current graph neural networks again graph structural/feature perturbations.
Students
- Yujia Wang (Ph.D., Spring 2022 - Present)
- Bochuan Cao (Ph.D., Fall 2022 - Present)
- Tianrong Zhang (Ph.D. co-advising with Dr. Prasenjit Mitra, Fall 2022 - Present)
- Yuanpu Cao (Ph.D., Fall 2023 - Present)
- Malcolm Zerbe (Undergrad, Fall 2023 - Present)
- Bharavi Misra (Undergrad, Fall 2023 - Present)
- Pei Fang (Research Intern, Fall 2021 - Present)
- Avi Bewtra (Undergrad, Fall 2021 - Spring 2022)
- Weiqi Peng (Research Intern, Fall 2021 - Spring 2022, Now at Amazon)
- Shuwen Chai (Research Intern, Fall 2021 - Spring 2022, Now a Ph.D. student at Northwestern University)
- Aryan Harshanan Patil (Undergrad, Fall 2022 - Spring 2023)
- Sirui Qi (Undergrad at PSU, Fall 2022 - Spring 2023)
- Sooraj Narayanan Sekar (Undergrad, Fall 2022 - Spring 2023)
Teaching
- Fall 2023: DS310: Machine Learning for Data Analytics
- Spring 2023: SRA221: Overview of Information Security
- Fall 2022: IST597: Special Topics on Adversarial Machine Learning
- Spring 2022: SRA221: Overview of Information Security
- Fall 2021: IST597: Special Topics on Adversarial Machine Learning
Acadamic Service
Senior Program Committee- International Joint Conference on Artificial Intelligence (IJCAI)
- AAAI Conference on Artificial Intelligence (AAAI)
- International Conference on Machine Learning (ICML)
- Neural Information Processing Systems (NeurIPS)
- International Conference on Learning Representations (ICLR)
- The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR)
- International Conference on Computer Vision (ICCV)
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
- International Conference on Artificial Intelligence and Statistics (AISTATS)
- The Conference on Uncertainty in Artificial Intelligence (UAI)
- IEEE International Conference on Big Data (BigData)
- Journal of Machine Learning Research (JMLR)
- IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
- IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
- IEEE Transactions on Knowledge and Data Engineering (TKDE)
- IEEE Signal Processing Letters (SPL)
- ACM Transactions on Knowledge Discovery from Data (TKDD)
- Journal of Artificial Intelligence Research (JAIR)
- Journal of Industrial and Management Optimization (JIMO)
- Journal of Computational and Applied Mathematics (ELSCAM)
- Neural Networks (NEUNET)
- BioData Mining (BIDM)
- Machine Learning (MACH)
- Mathematical Biosciences and Engineering (MBE)
- Reviews in Biomedical Engineering (RBME)
- Pattern Recognition Letters (PRLETTERS)
- Transactions on Big Data (TBD)
- Machine Learning and Knowledge Extraction (MAKE)
- Advanced Theory and Simulations
- Frontiers in Artificial Intelligence
- PLOS ONE
- PLOS Global Public Health
- Applied Science
- Algorithms
- Electronics
- Neurocomputing
- Entropy