| Causality learning: A new perspective for interpretable machine learning G Xu, TD Duong, Q Li, S Liu, X Wang arXiv preprint arXiv:2006.16789, 2020 | 42 | 2020 |
| From helpfulness prediction to helpful review retrieval for online product reviews C Vo, D Duong, D Nguyen, T Cao Proceedings of the 9th International Symposium on Information and …, 2018 | 19 | 2018 |
| Prototype-based counterfactual explanation for causal classification TD Duong, Q Li, G Xu arXiv preprint arXiv:2105.00703, 2021 | 14 | 2021 |
| Stochastic Intervention for Causal Effect Estimation T Dung Duong, Q Li, G Xu International Joint Conference on Neural Networks (IJCNN), arXiv: 2105.12898, 2021 | 12* | 2021 |
| Causal-aware generative imputation for automated underwriting Q Li, TD Duong, Z Wang, S Liu, D Wang, G Xu Proceedings of the 30th ACM International Conference on Information …, 2021 | 6 | 2021 |
| CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data Using Normalizing Flows TD Duong, Q Li, G Xu Pacific-Asia Conference on Knowledge Discovery and Data Mining, 133-144, 2023 | 2 | 2023 |
| Stochastic intervention for causal inference via reinforcement learning TD Duong, Q Li, G Xu Neurocomputing 482, 40-49, 2022 | 2 | 2022 |
| Achieving counterfactual fairness with imperfect structural causal model TD Duong, Q Li, G Xu Expert Systems with Applications 240, 122411, 2024 | | 2024 |
| Causality for Interpretable Machine Learning TD Duong | | 2023 |
| Causality-based Counterfactual Explanation for Classification Models TD Duong, Q Li, G Xu arXiv preprint arXiv:2105.00703, 2021 | | 2021 |
Qian LiLecturer (Assistant Professor), School of Electrical Engineering, Computing and MathematicalVerified email at curtin.edu.au
