| Joint entity linking with deep reinforcement learning Z Fang, Y Cao, Q Li, D Zhang, Z Zhang, Y Liu The World Wide Web Conference, 438-447, 2019 | 115 | 2019 |
| Be causal: De-biasing social network confounding in recommendation Q Li, X Wang, Z Wang, G Xu ACM Transactions on Knowledge Discovery from Data 17 (1), 1-23, 2023 | 70 | 2023 |
| How does knowledge graph embedding extrapolate to unseen data: a semantic evidence view R Li, Y Cao, Q Zhu, G Bi, F Fang, Y Liu, Q Li Proceedings of the AAAI Conference on Artificial Intelligence 36 (5), 5781-5791, 2022 | 66 | 2022 |
| Causality Learning: A New Perspective for Interpretable Machine Learning G Xu, TD Duong, Q Li, S Liu, X Wang The IEEE Intelligent Informatics Bulletin 20, 2020 | 58 | 2020 |
| Causal disentanglement for semantic-aware intent learning in recommendation X Wang, Q Li, D Yu, P Cui, Z Wang, G Xu IEEE Transactions on Knowledge and Data Engineering 35 (10), 9836-9849, 2022 | 42 | 2022 |
| Causal optimal transport for treatment effect estimation Q Li, Z Wang, S Liu, G Li, G Xu IEEE transactions on neural networks and learning systems 34 (8), 4083-4095, 2021 | 29 | 2021 |
| RLINK: Deep reinforcement learning for user identity linkage X Li, Y Cao, Q Li, Y Shang, Y Li, Y Liu, G Xu World Wide Web 24, 85-103, 2021 | 28 | 2021 |
| Learning with privileged information for photo aesthetic assessment Y Shu, Q Li, S Liu, G Xu Neurocomputing 404, 304-316, 2020 | 28 | 2020 |
| Leveraging multi-level dependency of relational sequences for social spammer detection J Yin, Q Li, S Liu, Z Wu, G Xu Neurocomputing 428, 130-141, 2021 | 26 | 2021 |
| Reinforced path reasoning for counterfactual explainable recommendation X Wang, Q Li, D Yu, Q Li, G Xu IEEE Transactions on Knowledge and Data Engineering, 2024 | 23 | 2024 |
| Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning H Yang, H Chen, S Zhang, X Sun, Q Li, X Zhao, G Xu Proceedings of the ACM Web Conference 2023, 621-629, 2023 | 20 | 2023 |
| Polynomial representation for persistence diagram Z Wang, Q Li, G Li, G Xu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 19 | 2019 |
| Boosting imbalanced data learning with Wiener process oversampling Q Li, G Li, W Niu, Y Cao, L Chang, J Tan, L Guo Frontiers of Computer Science 11, 836-851, 2017 | 18 | 2017 |
| Deconfounded recommendation via causal intervention D Yu, Q Li, X Wang, G Xu Neurocomputing 529, 128-139, 2023 | 17 | 2023 |
| SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation B Li, T Guo, X Zhu, Q Li, Y Wang, F Chen Proceedings of the Sixteenth ACM International Conference on Web Search and …, 2023 | 17 | 2023 |
| Deep treatment-adaptive network for causal inference Q Li, Z Wang, S Liu, G Li, G Xu The VLDB Journal 31 (5), 1127-1142, 2022 | 14 | 2022 |
| Prototype-based Counterfactual Explanation for Causal Classification TD Duong, Q Li, G Xu arXiv preprint arXiv:2105.00703, 2021 | 14 | 2021 |
| Riemannian submanifold tracking on low-rank algebraic variety Q Li, Z Wang Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 14 | 2017 |
| Lingo: linearized grassmannian optimization for nuclear norm minimization Q Li, W Niu, G Li, Y Cao, J Tan, L Guo Proceedings of the 24th ACM International on Conference on Information and …, 2015 | 14 | 2015 |
| Hilbert Sinkhorn Divergence for Optimal Transport Q Li, Z Wang, G Li, J Pang, G Xu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 13 | 2021 |
Guandong Xu, PhDChair Professor EdUHK, Professor UTSuts.edu.au의 이메일 확인됨