| 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 | 30* | 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 | 24 | 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 | 22 | 2022 |
| Propensity Matters: Measuring and Enhancing Balancing for Recommendation H Li, Y Xiao, C Zheng, P Wu, P Cui International Conference on Machine Learning, 2023 | 14 | 2023 |
| 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 | 11 | 2023 |
| 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 | 9 | 2023 |
| 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 | 7 | 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 | 5 | 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 | 4 | 2024 |
| 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 | 1 | 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 | | 2024 |
| PTiBK H h2 iK2Mi _2; BK2b G2MBM; 7 _2+ QKK2M/iBQM H Li, C Zheng, P Wu, K Kuang, Y Liu, P Cui | | 2024 |
| Adaptive Causal Balancing for Collaborative Filtering H Li, Y Xiao, C Zheng, P Wu, X Chen, Z Geng, P Cui The Twelfth International Conference on Learning Representations, 2023 | | 2023 |
| Rethinking Counterfactual Fairness: On Which Individuals to Enforce, and How? H Li, C Zheng, Z Jiang, Z Fang, Y Liu, Z Geng, K Zhang | | 2023 |
| 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 | | 2023 |
| 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 | | |
| 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 The Twelfth International Conference on Learning Representations, 0 | | |
Peng WuTechnology and Business UniversityVerified email at btbu.edu.cn
