Publications

(underline indicates student advisee, * indicates equal contribution)

Preprints

  1. Fair In-Context Learning via Latent Concept Variables.
    Karuna Bhaila, Minh-Hao Van, Kennedy Edemacu, Chen Zhao, Feng Chen, Xintao Wu.
    ArXiv preprint, 2411.02671
  2. FADE: Towards Fairness-aware Augmentation for Domain Generalization via Classifier-Guided Score-based Diffusion Models.
    Yujie Lin, Dong Li, Chen Zhao, Minglai Shao.
    ArXiv preprint, 2406.09495
  3. Graphs Generalization under Distribution Shifts.
    Qin Tian, Wenjun Wang, Chen Zhao, Minglai Shao, Wang Zhang, Dong Li.
    ArXiv preprint, 2403.16334
  4. Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective.
    Haoliang Wang, Chen Zhao, Yunhui Guo, Kai Jiang, Feng Chen.
    ArXiv preprint, 2309.10209
  5. PDFM: A Primal-Dual Fairness-Aware Framework for Meta-learning.
    Chen Zhao, Feng Chen, Zhuoyi Wang, Latifur Khan.
    ArXiv preprint, 2009.12675v2

Journal Papers

  1. [DLT] An Automated Vulnerability Detection Framework for Smart Contracts.
    Feng Mi, Chen Zhao, Zhuoyi Wang, Sadaf MD Halim, Xiaodi Li, Zhouxiang Wu, Latifur Khan, Bhavani Thuraisingham.
    The ACM Distributed Ledger Technologies: Research and Practice, 2024
  2. [Frontier in Big Data] Efficient Out-of-Distribution Detection via Layer-Adaptive Scoring and Early Stopping.
    Haoliang Wang, Chen Zhao, Feng Chen.
    Frontiers in Big Data, section Data Mining and Management, 2024
  3. [TKDD] Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness.
    Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen.
    The ACM Transactions on Knowledge Discovery from Data, 2024
  4. [Development] CDC42 is required for epicardial and pro-epicardial development by mediating FGF receptor trafficking to plasma membrane.
    Jingjing Li, Lianjie Miao, Chen Zhao, Wasay Mohiuddin Shaikh Qureshi, David Shieh, Hua Guo, Yangyang Lu, Saiyang Hu, Alice Huang, Lu Zhang, Chen-leng Cai, Leo Q. Wan, Hongbo Xin, Peter Vincent, Harold A. Singer, Yi Zheng, Ondine Cleaver, Zhen-Chuan Fan, Mingfu Wu.
    Development, 147-173, 2017
  5. [Development] Numb family proteins are essential for cardiac morphogenesis and progenitor differentiation.
    Chen Zhao, Hua Guo, Jingjing Li, Thomas Myint, William Pittman, Le Yang, Weimin Zhong, Robert J. Schwartz, John J. Schwarz, Harold A. Singer, Michelle D. Tallquist, Mingfu Wu.
    Development, 141(2):281-95, 2014

Conference Papers

KDD(5), IJCAI(2), AAAI(3), CVPR(1), WWW(1), CIKM(3), SDM(1), ICDM(2), ECAI(1), ICASSP(3), IEEE BigData(3), PAKDD(1), ICBC(1), ICKG(2), IGARSS(1)

  1. [ICASSP'25] GDDA: Semantic OOD Detection on Graphs under Covariate Shift via Score-Based Diffusion Model.
    Zhixia He, Chen Zhao, Minglai Shao, Yujie Lin, Dong Li, Qin Tian
    In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2025
  2. [ICASSP'25] Hypergraph-Based Dynamic Graph Node Classification.
    Xiaoxu Ma, Chen Zhao, Minglai Shao, Yujie Lin
    In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2025
  3. [SDM'25] Evidence-Based Out-of-Distribution Detection on Multi-Label Graphs.
    Ruomeng Ding, Xujiang Zhao, Chen Zhao, Minglai Shao, Zhengzhang Chen, Haifeng Chen
    In Proceedings of the SIAM International Conference on Data Mining, 2025
  4. [AAAI'25] Metric-Agnostic Continual Learning for Sustainable Group Fairness.
    Heng Lian, Chen Zhao, Zhong Chen, Xingquan Zhu, My T. Thai, Yi He
    In Proceedings of the AAAI Conference on Artificial Intelligence, 2025
  5. [AAAI'25] Multi-View Unsupervised Column Subset Selection via Combinatorial Search.
    Guihong Wan, Ninghui Hao, Crystal Maung, Haim Schweitzer, Chen Zhao, Kun-Hsing Yu, Yevgeniy Semenov
    In Proceedings of the AAAI Conference on Artificial Intelligence, 2025
  6. [KDD'25] MLDGG: Meta-Learning for Domain Generalization on Graphs.
    Qin Tian, Chen Zhao, Minglai Shao, Wenjun Wang, Yujie Lin, Dong Li
    In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025
  7. [BigData'24] Feature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set Domain Generalization.
    Haoliang Wang, Chen Zhao, Feng Chen
    In Proceedings of the IEEE International Conference on Big Data, 2024
  8. [BigData'24] MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning.
    Haoliang Wang, Chen Zhao, Feng Chen
    In Proceedings of the IEEE International Conference on Big Data, 2024
  9. [BigData'24] FEED: Fairness-Enhanced Meta-Learning for Domain Generalization.
    Kai Jiang, Chen Zhao, Haoliang Wang, Feng Chen
    In Proceedings of the IEEE International Conference on Big Data, 2024
  10. [CIKM'24] Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously.
    Dong Li, Chen Zhao, Minglai Shao, Wenjun Wang.
    In Proceedings of the ACM International Conference on Information and Knowledge Management, 2024
  11. [ECAI'24] Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models.
    Aneesh Komanduri, Chen Zhao, Feng Chen, Xintao Wu.
    In Proceedings of the European Conference on Artificial Intelligence, 2024
  12. [KDD'24] Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously.
    Chen Zhao*, Kai Jiang*, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Earl Grant, Feng Chen.
    In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024
  13. [IJCAI'24] Supervised Algorithmic Fairness in Distribution Shifts: A Survey.
    Minglai Shao*, Dong Li*, Chen Zhao*, Xintao Wu, Yujie Lin, Qin Tian.
    In Proceedings of the 33rd International Joint Conference on Artificial Intelligence, 2024
  14. [IJCAI'24] Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments.
    Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen.
    In Proceedings of the 33rd International Joint Conference on Artificial Intelligence, 2024
  15. [IGARSS'24] Uncovering Bias in Building Damage Assessment from Satellite Imagery.
    Dennis Melamed, Cameron Johnson, Isaac D. Gerg, Chen Zhao, Russell Blue, Philip Morrone, Anthony Hoogs, Brian Clipp.
    In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2024
  16. [CIKM'23] Adaptation Speed Analysis for Fairness-aware Causal Models.
    Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen.
    In Proceedings of the ACM International Conference on Information and Knowledge Management, 2023
  17. [CIKM'23] Contrastive Representation Learning Based on Multiple Node-centered Subgraphs.
    Dong Li, Wenjun Wang, Minglai Shao, Chen Zhao.
    In Proceedings of the ACM International Conference on Information and Knowledge Management, 2023
  18. [KDD'23] Towards Fair Disentangled Online Learning for Changing Environments.
    Chen Zhao*, Feng Mi*, Xintao Wu, Kai Jiang, Latifur Khan, Christan Earl Grant, Feng Chen.
    In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
  19. [CVPR'23] Open Set Action Recognition via Multi-Label Evidential Learning.
    Chen Zhao, Dawei Du, Anthony Hoogs, Christopher Funk.
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
  20. [ICASSP'23] Multi-Label Temporal Evidential Neural Networks for Early Event Detection.
    Xujiang Zhao, Xuchao Zhang, Chen Zhao, Jin-Hee Cho, Lance Kaplan, Dong Hyun Jeong, Audun Jøsang, Haifeng Chen, Feng Chen.
    In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2023
  21. [KDD'22] Adaptive Fairness-Aware Online Meta-Learning for Changing Environments.
    Chen Zhao*, Feng Mi*, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen.
    In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022
  22. [PAKDD'22] Layer Adaptive Deep Neural Networks for Out-of-distribution Detection.
    Haoliang Wang, Chen Zhao, Xujiang Zhao, Feng Chen.
    In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2022
  23. [AAAI'22] A Nested Bi-level Optimization Framework for Robust Few-Shot Learning.
    Krishnateja Killamsetty, Changbin Li, Chen Zhao, Rishabh Krishnan Iyer, Feng Chen.
    In Proceedings of the AAAI Conference on Artificial Intelligence, 2022
  24. [KDD'21] Fairness-Aware Online Meta-learning.
    Chen Zhao, Feng Chen, Bhavani Thuraisingham.
    In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021
  25. [ICBC'21] VSCL: Automating Vulnerability Detection in Smart Contracts with Deep Learning.
    Feng Mi, Zhuoyi Wang, Chen Zhao, Jinghui Guo, Fawaz Ahmed, Latifur Khan.
    In Proceedings of the IEEE International Conference on Blockchain and Cryptocurrency, 2021
  26. [WWW'21] CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining.
    Zhuoyi Wang, Yuqiao Chen, Chen Zhao, Hemeng Tao, Yu Lin, Xujiang Zhao, Yigong Wang and Latifur Khan.
    In Proceedings of the ACM International World Wide Web Conference, 2021
  27. [ICDM'20] A Primal-Dual Subgradient Approach for Fair Meta Learning.
    Chen Zhao, Feng Chen, Zhuoyi Wang, Latifur Khan.
    In Proceedings of the IEEE International Conference on Data Mining, 2020
  28. [ICKG'20] Fair Meta-Learning for Few-Shot Classification.
    Chen Zhao, Changbin Li, Jincheng Li, Feng Chen.
    In Proceedings of the IEEE International Conference on Knowledge Graph, 2020
  29. [ICKG'20] Unfairness Discovery and Prevention for Few-Shot Regression.
    Chen Zhao, Feng Chen.
    In Proceedings of the IEEE International Conference on Knowledge Graph, 2020
  30. [ICDM'19] Rank-Based Multi-task Learning for Fair Regression.
    Chen Zhao, Feng Chen.
    In Proceedings of the IEEE International Conference on Data Mining, 2019

Workshop Papers

  1. [CVPR'24-W] Causal Diffusion Autoencoders: Toward Representation-Enabled Counterfactual Generation via Diffusion Probabilistic Models.
    Aneesh Komanduri, Chen Zhao, Feng Chen, Xintao Wu.
    CVPR Workshop on Generative Models for Computer Vision, 2024
  2. [KDD'24-W] Semantic OOD Detection under Covariate Shift on Graphs with Diffusion Model.
    Zhixia He, Chen Zhao, Minglai Shao, Yujie Lin, Dong Li.
    KDD Workshop on Uncertainty Reasoning and Quantification in Decision Making, 2024
  3. [KDD'24-W] IDGG: Invariant Learning for Out-of-Distribution Generalization on Graphs.
    Qin Tian, Wenjun Wang, Minglai Shao, Chen Zhao, Dong Li.
    KDD Workshop on Uncertainty Reasoning and Quantification in Decision Making, 2024
  4. [KDD'24-W] HyperDG: A Hypergraph-Based Approach for Dynamic Graph Node Classification under Spatio-Temporal Shift.
    Xiaoxu Ma, Chen Zhao, Minglai Shao.
    KDD Workshop on Uncertainty Reasoning and Quantification in Decision Making, 2024
  5. [KDD'24-W] Out-of-Distribution Detection for Heterogeneous Graph Neural Networks.
    Tao Yin, Chen Zhao, Minglai Shao.
    KDD Workshop on Uncertainty Reasoning and Quantification in Decision Making, 2024
  6. [KDD'24-W] Fair Data Generation via Score-based Diffusion Model.
    Yujie Lin, Dong Li, Chen Zhao, Minglai Shao.
    KDD workshop on Ethical Artificial Intelligence: Methods and Applications, 2024
  7. [NeurIPS'23-W] Achieving Counterfactual Fairness in Changing Environments via Sequential Autoencoder.
    Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Baoluo Meng, Haifeng Chen.
    NeurIPS Workshop on Algorithmic Fairness through the Lens of Time, 2023
  8. [KDD'23-W] Learning Fair and Domain Generalization Representation.
    Dong Li, Chen Zhao, Minglai Shao, Xujiang Zhao.
    KDD workshop on Ethical Artificial Intelligence: Methods and Applications, 2023
  9. [KDD'23-W] Adaptation Speed of Causal Models Concerning Fairness.
    Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen.
    KDD workshop on Ethical Artificial Intelligence: Methods and Applications, 2023
  10. [AAAI'23-W] Detecting Multi-Label Out-of-Distribution Nodes on Graphs.
    Ruomeng Ding, Xujiang Zhao, Chen Zhao, Minglai Shao.
    AAAI workshop on Uncertainty Reasoning and Quantification in Decision Making, 2023
  11. [NeurIPS’21-W] A Reweighted Meta Learning Framework for Robust Few Shot Learning.
    Krishnateja Killamsetty, Changbin Li, Chen Zhao, Rishabh Krishnan Iyer, Feng Chen.
    NeurIPS Workshop on Meta-Learning, 2021