| StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random H Li, C Zheng, P Wu The Eleventh International Conference on Learning Representations, 2023 | 59* | 2023 |
| Propensity Matters: Measuring and Enhancing Balancing for Recommendation H Li, Y Xiao, C Zheng, P Wu, P Cui International Conference on Machine Learning, 2023 | 42 | 2023 |
| Balancing unobserved confounding with a few unbiased ratings in debiased recommendations H Li, Y Xiao, C Zheng, P Wu Proceedings of the ACM Web Conference 2023, 1305-1313, 2023 | 41 | 2023 |
| TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations H Li, Y Lyu, C Zheng, P Wu The Eleventh International Conference on Learning Representations, 2022 | 41 | 2022 |
| Removing hidden confounding in recommendation: a unified multi-task learning approach H Li, K Wu, C Zheng, Y Xiao, H Wang, Z Geng, F Feng, X He, P Wu Advances in Neural Information Processing Systems 36, 2024 | 26 | 2024 |
| Trustworthy Policy Learning under the Counterfactual No-Harm Criterion H Li, C Zheng, Y Cao, Z Geng, Y Liu, P Wu International Conference on Machine Learning, 2023 | 24 | 2023 |
| Debiased collaborative filtering with kernel-based causal balancing H Li, C Zheng, Y Xiao, P Wu, Z Geng, X Chen, P Cui arXiv preprint arXiv:2404.19596, 2024 | 15 | 2024 |
| Who should be given incentives? counterfactual optimal treatment regimes learning for recommendation H Li, C Zheng, P Wu, K Kuang, Y Liu, P Cui Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 13 | 2023 |
| Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference H Li, C Zheng, S Ding, P Wu, Z Geng, F Feng, X He arXiv preprint arXiv:2404.19620, 2024 | 10 | 2024 |
| Debiased recommendation with noisy feedback H Li, C Zheng, W Wang, H Wang, F Feng, XH Zhou Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | 9 | 2024 |
| Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering H Li, C Zheng, S Wang, K Wu, E Wang, P Wu, Z Geng, X Chen, XH Zhou Forty-first International Conference on Machine Learning, 0 | 9 | |
| Uncovering the propensity identification problem in debiased recommendations H Zhang, S Wang, H Li, C Zheng, X Chen, L Liu, S Luo, P Wu 2024 IEEE 40th International Conference on Data Engineering (ICDE), 653-666, 2024 | 8 | 2024 |
| Pareto Invariant Representation Learning for Multimedia Recommendation S Huang, H Li, Q Li, C Zheng, L Liu Proceedings of the 31th ACM International Conference on Multimedia, 2023 | 8 | 2023 |
| ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction H Li, T Hu, Z Xiong, C Zheng, F Feng, X He, XH Zhou Proceedings of the 17th ACM Conference on Recommender Systems, 2023 | 3 | 2023 |
| A Two-Stage Pretraining-Finetuning Framework for Treatment Effect Estimation with Unmeasured Confounding C Zhou, Y Li, C Zheng, H Zhang, M Zhang, H Li, M Gong arXiv preprint arXiv:2501.08888, 2025 | | 2025 |
| Sampling Process Brings Additional Bias for Debiased Recommendation C Zheng, H Li, H Wang, C Zhou, X Chen, M Gong | | |
| Towards Relaxing the Unbiasedness Condition of Doubly Robust Estimators for Debiased Recommendation H Li, C Zheng, S Wang, K Wu, E Wang, X Chen, Z Geng, P Wu | | |
| Rethinking Counterfactual Fairness: On Which Individuals to Enforce, and How? H Li, C Zheng, Z Jiang, Z Fang, Y Liu, Z Geng, K Zhang | | |
| From Deterministic to Probabilistic World: Balancing Enhanced Doubly Robust Learning for Debiased Recommendation H Li, C Zheng, Y Xiao, M Zhang, X Chen, XH Zhou | | |
Haoxuan LiPeking UniversityVerified email at stu.pku.edu.cn