| 2025 | AAAI | Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks. | Erhu He, Declan Kutscher, Yiqun Xie, Jacob Zwart, Zhe Jiang, Huaxiu Yao, Xiaowei Jia |
| 2024 | AAAI | Referee-Meta-Learning for Fast Adaptation of Locational Fairness. | Weiye Chen, Yiqun Xie, Xiaowei Jia, Erhu He, Han Bao, Bang An, Xun Zhou |
| 2024 | AAAI | Fair Graph Learning Using Constraint-Aware Priority Adjustment and Graph Masking in River Networks. | Erhu He, Yiqun Xie, Alexander Y. Sun, Jacob Zwart, Jie Yang, Zhenong Jin, Yang Wang, Hassan A. Karimi, Xiaowei Jia |
| 2024 | KDD | PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization. | Yuyang Ye, Lu-An Tang, Haoyu Wang, Runlong Yu, Wenchao Yu, Erhu He, Haifeng Chen, Hui Xiong |
| 2024 | SDM | Knowledge Guided Machine Learning for Extracting, Preserving, and Adapting Physics-aware Features. | Erhu He, Yiqun Xie, Licheng Liu, Zhenong Jin, Dajun Zhang, Xiaowei Jia |
| 2023 | AAAI | Physics Guided Neural Networks for Time-Aware Fairness: An Application in Crop Yield Prediction. | Erhu He, Yiqun Xie, Licheng Liu, Weiye Chen, Zhenong Jin, Xiaowei Jia |
| 2023 | IJCAI | CGS: Coupled Growth and Survival Model with Cohort Fairness. | Erhu He, Yue Wan, Benjamin H. Letcher, Jennifer H. Fair, Yiqun Xie, Xiaowei Jia |
| 2022 | AAAI | Fairness by "Where": A Statistically-Robust and Model-Agnostic Bi-level Learning Framework. | Yiqun Xie, Erhu He, Xiaowei Jia, Weiye Chen, Sergii Skakun, Han Bao, Zhe Jiang, Rahul Ghosh, Praveen Ravirathinam |
| 2022 | IJCAI | Statistically-Guided Deep Network Transformation to Harness Heterogeneity in Space (Extended Abstract). | Yiqun Xie, Erhu He, Xiaowei Jia, Han Bao, Xun Zhou, Rahul Ghosh, Praveen Ravirathinam |
| 2021 | ICDM | A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity. | Yiqun Xie, Erhu He, Xiaowei Jia, Han Bao, Xun Zhou, Rahul Ghosh, Praveen Ravirathinam |