| 2025 | AAAI | Fair Graph U-Net: A Fair Graph Learning Framework Integrating Group and Individual Awareness. | Zichong Wang, Zhibo Chu, Thang Viet Doan, Shaowei Wang, Yongkai Wu, Vasile Palade, Wenbin Zhang |
| 2025 | ICDM | FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agents. | Yucong Dai, Lu Zhang, Feng Luo, Mashrur Chowdhury, Yongkai Wu |
| 2025 | ICLR | Towards counterfactual fairness through auxiliary variables. | Bowei Tian, Ziyao Wang, Shwai He, Wanghao Ye, Guoheng Sun, Yucong Dai, Yongkai Wu, Ang Li |
| 2024 | AAAI | Long-Term Fair Decision Making through Deep Generative Models. | Yaowei Hu, Yongkai Wu, Lu Zhang |
| 2024 | IJCAI | Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms. | Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu |
| 2024 | IJCNN | Fair Weak-Supervised Learning: A Multiple-Instance Learning Approach. | Yucong Dai, Xiangyu Jiang, Yaowei Hu, Lu Zhang, Yongkai Wu |
| 2024 | IJCNN | Achieving Equalized Explainability Through Data Reconstruction. | Shuang Wang, Yongkai Wu |
| 2024 | IJCNN | Achieving Fairness through Constrained Recourse. | Shuang Wang, Yongkai Wu |
| 2024 | SDM | Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach. | Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu |
| 2023 | CIKM | On Root Cause Localization and Anomaly Mitigation through Causal Inference. | Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan |
| 2023 | IJCNN | Neural Time-Invariant Causal Discovery from Time Series Data. | Saima Absar, Yongkai Wu, Lu Zhang |
| 2023 | IJCNN | Fair Selection through Kernel Density Estimation. | Xiangyu Jiang, Yucong Dai, Yongkai Wu |
| 2023 | PAKDD | Achieving Counterfactual Fairness for Anomaly Detection. | Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan |
| 2021 | AAAI | A Generative Adversarial Framework for Bounding Confounded Causal Effects. | Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu |
| 2020 | WWW | Fairness through Equality of Effort. | Wen Huang, Yongkai Wu, Lu Zhang, Xintao Wu |
| 2019 | IJCAI | Counterfactual Fairness: Unidentification, Bound and Algorithm. | Yongkai Wu, Lu Zhang, Xintao Wu |
| 2019 | IJCAI | Achieving Causal Fairness through Generative Adversarial Networks. | Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, Xintao Wu |
| 2019 | WWW | On Convexity and Bounds of Fairness-aware Classification. | Yongkai Wu, Lu Zhang, Xintao Wu |
| 2018 | IJCAI | Achieving Non-Discrimination in Prediction. | Lu Zhang, Yongkai Wu, Xintao Wu |
| 2018 | KDD | On Discrimination Discovery and Removal in Ranked Data using Causal Graph. | Yongkai Wu, Lu Zhang, Xintao Wu |
| 2017 | IJCAI | A Causal Framework for Discovering and Removing Direct and Indirect Discrimination. | Lu Zhang, Yongkai Wu, Xintao Wu |
| 2017 | KDD | Achieving Non-Discrimination in Data Release. | Lu Zhang, Yongkai Wu, Xintao Wu |
| 2016 | DSAA | Using Loglinear Model for Discrimination Discovery and Prevention. | Yongkai Wu, Xintao Wu |
| 2016 | IJCAI | Situation Testing-Based Discrimination Discovery: A Causal Inference Approach. | Lu Zhang, Yongkai Wu, Xintao Wu |