| Graphlime: Local interpretable model explanations for graph neural networks Q Huang, M Yamada, Y Tian, D Singh, Y Chang IEEE Transactions on Knowledge and Data Engineering, 2022 | 336 | 2022 |
| Unsupervised nonlinear feature selection from high-dimensional signed networks Q Huang, T Xia, H Sun, M Yamada, Y Chang Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4182-4189, 2020 | 10 | 2020 |
| IMBENS: Ensemble class-imbalanced learning in Python Z Liu, J Kang, H Tong, Y Chang arXiv preprint arXiv:2111.12776, 2021 | 8 | 2021 |
| SemiITE: Semi-supervised Individual Treatment Effect Estimation via Disagreement-Based Co-training Q Huang, J Ma, J Li, H Sun, Y Chang ECML-PKDD 2022, 2022 | 2 | 2022 |
| A causality-inspired feature selection method for cancer imbalanced high-dimensional data Y Liu, Q Huang, H Sun, Y Chang bioRxiv, 2021.10. 04.462984, 2021 | 1 | 2021 |
| Multi-Classification of Cancer Samples Based on Co-Expression Analyses H Jiang, Q Huang, L Chen, Z Li, Y Xu, H Sun, Y Chang 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2019 | 1 | 2019 |
| Modeling Interference for Individual Treatment Effect Estimation from Networked Observational Data Q Huang, J Ma, J Li, R Guo, H Sun, Y Chang ACM Transactions on Knowledge Discovery from Data 18 (3), 1-21, 2023 | | 2023 |
| Extracting Post-Treatment Covariates for Heterogeneous Treatment Effect Estimation Q Huang, D Cao, Y Chang, Y Liu | | 2023 |
