Jun Wu

Jun Wu

Assistant Professor

  1. Katherine Tieu, Dongqi Fu, Jun Wu, and Jingrui He. “Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem.” AISTATS 2025.
  2. Jun Wu, Jingrui He, and Hanghang Tong. “Distributional Network of Networks for Modeling Data Heterogeneity.” KDD 2024.
  3. Wenxuan Bao, Jun Wu, and Jingrui He. “BOBA: Byzantine-Robust Federated Learning with Label Skewness.” AISTATS 2024.
  4. Jun Wu, Elizabeth Ainsworth, Andrew Leakey, Haixun Wang, and Jingrui He. “Graph-Structured Gaussian Processes for Transferable Graph Learning.” NeurIPS 2023.
  5. Jun Wu, and Jingrui He. “Trustworthy Transfer Learning: Transferability and Trustworthiness.” KDD 2023.
  6. Jun Wu, Wenxuan Bao, Elizabeth Ainsworth, and Jingrui He. “Personalized Federated Learning with Parameter Propagation.” KDD 2023.
  7. Wenxuan Bao, Haohan Wang, Jun Wu, and Jingrui He. “Optimizing the Collaboration Structure in Cross-Silo Federated Learning.” ICML 2023.
  8. Jun Wu, Jingrui He, and Elizabeth Ainsworth. “Non-IID Transfer Learning on Graphs.” AAAI 2023.
  9. Sheng Wang, Kaiyu Guan, Chenhui Zhang, Chongya Jiang, Qu Zhou, Kaiyuan Li, and Ziqi Qin, Elizabeth A. Ainsworth, Jingrui He, Jun Wu, Dan Schaefer, Lowell E. Gentry, Andrew J. Margenot, and Leo Herzberger. “Airborne Hyperspectral Imaging of Cover Crops Through Radiative Transfer Process-Guided Machine Learning.” Remote Sensing of Environment 2023.
  10. Jun Wu, and Jingrui He. “A Unified Framework for Adversarial Attacks on Multi-Source Domain Adaptation.” TKDE 2023.
  11. Jun Wu, Jingrui He, Sheng Wang, Kaiyu Guan, and Elizabeth Ainsworth. “Distribution-Informed Neural Networks for Domain Adaptation Regression.” NeurIPS 2022.
  12. Jun Wu, and Jingrui He. “Domain Adaptation with Dynamic Open-Set Targets.” KDD 2022.
  13. Jun Wu, and Jingrui He. “A Unified Meta-Learning Framework for Dynamic Transfer Learning.” IJCAI 2022.
  14. Jun Wu, Hanghang Tong, Elizabeth Ainsworth, and Jingrui He. “Adaptive Knowledge Transfer on Evolving Domains.” IEEE BigData 2022.
  15. Jun Wu, and Jingrui He. “Dynamic Transfer Learning with Progressive Meta-task Scheduler.” Frontiers in Big Data 2022.
  16. Yao Zhou, Jun Wu, Haixun Wang, and Jingrui He. “Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning.” CIKM 2022.
  17. Jun Wu, and Jingrui He. “Indirect Invisible Poisoning Attacks on Domain Adaptation.” KDD 2021.
  18. Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi, Evren Korpeoglu, Kannan Achan, and Jingrui He. “PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network.” KDD 2021.
  19. Shane Roach, Connie Ni, Alexei Kopylov, Tsai Ching Lu, Jiejun Xu, Si Zhang, Boxin Du, Dawei Zhou, Jun Wu, Lihui Liu, Yuchen Yan, Jingrui He, and Hanghang Tong. “CANON: Complex Analytics of Network of Networks for Modeling Adversarial Activities.” IEEE BigData 2020.
  20. Jun Wu, and Jingrui He. “Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy.” CIKM 2019.
  21. Jun Wu, Jingrui He, and Jiejun Xu. “DEMO-Net: Degree-Specific Graph Neural Networks for Node and Graph Classification.” KDD 2019.
  22. Jun Wu, Jingrui He, and Yongming Liu. “Imverde: Vertex-Diminished Random Walk for Learning Imbalanced Network Representation.” IEEE BigData 2018.