| On the opportunity of causal learning in recommendation systems: Foundation, estimation, prediction and challenges P Wu, H Li, Y Deng, W Hu, Q Dai, Z Dong, J Sun, R Zhang, XH Zhou International Joint Conference on Artificial Intelligence (2022), 2022 | 50 | 2022 |
| Addressing unmeasured confounder for recommendation with sensitivity analysis S Ding, P Wu, F Feng, Y Wang, X He, Y Liao, Y Zhang Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 33 | 2022 |
| 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 | 28* | 2023 |
| A generalized doubly robust learning framework for debiasing post-click conversion rate prediction Q Dai, H Li, P Wu, Z Dong, XH Zhou, R Zhang, R Zhang, J Sun Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 28 | 2022 |
| 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, 2023 | 21 | 2023 |
| Multiple robust learning for recommendation H Li, Q Dai, Y Li, Y Lyu, Z Dong, XH Zhou, P Wu Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 4417-4425, 2023 | 18 | 2023 |
| Causal recommendation: Progresses and future directions W Wang, Y Zhang, H Li, P Wu, F Feng, X He Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 14 | 2023 |
| Propensity Matters: Measuring and Enhancing Balancing for Recommendation H Li, Y Xiao, C Zheng, P Wu, P Cui Proceedings of the 40-th International Conference on Machine Learning, 2023 | 12 | 2023 |
| Propensity score regression for causal inference with treatment heterogeneity P Wu, SS Han, X Tong, R Li Statistica Sinica, 2022 | 11 | 2022 |
| Trustworthy Policy Learning under the Counterfactual No-Harm Criterion H Li, C Zheng, Y Cao, Z Geng, Y Liu, P Wu Proceedings of the 40-th International Conference on Machine Learning, 2023 | 10 | 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 |
| Model-assisted inference for covariate-specific treatment effects with high-dimensional data P Wu, Z Tan, W Hu, XH Zhou Statistica Sinica, 2022 | 6 | 2022 |
| Semiparametric estimation for average causal effects using propensity score-based spline P Wu, X Xu, X Tong, Q Jiang, B Lu Journal of Statistical Planning and Inference 212, 153-168, 2021 | 6 | 2021 |
| Regression and subgroup detection for heterogeneous samples B Liang, P Wu, X Tong, Y Qiu Computational Statistics 35, 1853-1878, 2020 | 6 | 2020 |
| Identification and estimation of treatment effects on long-term outcomes in clinical trials with external observational data W Hu, X Zhou, P Wu Statistica Sinica, 2023 | 5 | 2023 |
| 基于 Copula 函数的海南热带气旋风雨联合概率特征分析 侯静惟， 方伟华， 程锰， 叶妍婷， 吴鹏， 韩轶男 自然灾害学报 28 (3), 54-64, 2019 | 5 | 2019 |
| 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 | 3 | 2024 |
| In-flight spectral response function retrieval of a multi-spectral radiometer based on the functional data analysis technique LC Na Xu, Peng Wu, Gang Ma, Qirui Hu, Xiuqing Hu, Ronghua Wu, Yunfeng Wang ... IEEE Transactions on Geoscience and Remote Sensing 60, 1-10, 2021 | 3 | 2021 |
Fuli FengUniversity of Science and Technology of China在 ustc.edu.cn 的电子邮件经过验证
