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数学与统计学院

吴鹏

吴 鹏

邮箱: pengwu@btbu.edu.cn

通讯地点:北京市良乡高教园区鸿运国际-数学与统计学院楼209

研究兴趣:因果推断, 推荐系统, 机械学习

个人主页:https://pengwu.site


个人经历

2011.9-2015.7,江西财经大学,统计学院,统计学,本科

2015.9-2017.7,北京师范大学,数学与科学学院,概率论与数理统计,硕士

2017.9-2020.7,北京师范大学,统计学院,应用统计,博士

2020.7-2022.6,北京大学,北京国际数学研究中心,博士后

2022.6至今,鸿运国际,数学与统计学院,副教授


科研项目

2024.1-2026.12,主持国家自然科学基金青年基金,“基于数据融合的恒久因果效应研究”,编号1230011066


社会兼职

中国现场统计研究会因果推断分会理事,北京生物医学统计与数据治理研究会理事,ACM会员


科研论文(*corresponding author, #contributed equally

2023

[1] Wenjie Hu, Xiao-Hua Zhou, and Peng Wu* (2023), Identification and estimation of treatment effects on long-term outcomes in clinical trials with external observational data. Statistica Sinica
 
[2] Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Hao Wang, Fuli Feng, Xiangnan He, Zhi Geng, and Peng Wu* (2023), Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 23)
 
[3] Jinqiu Jin, Haoxuan Li, Fuli Feng, Sihao Ding, Peng Wu, and Xiangnan He (2023), Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 23)
 

[4] Haoxuan Li, Chunyuan Zheng, Peng Wu, Kun Kuang, Yue Liu, Peng Cui (2023), Who should be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. (KDD 23)


[5] Haoxuan Li, Chunyuan Zheng, Yixiao Cao, Zhi Geng, Yue Liu*, and Peng Wu* (2023), Trustworthy Policy Learning under the Counterfactual No-Harm Criterion. Fortieth International Conference on Machine Learning (ICML 23)


[6] Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu*, and Peng Cui (2023), Propensity Matters: Measuring and Enhancing Balancing for Recommendation. Fortieth International Conference on Machine Learning (ICML 23)


[7] Wenjie Wang, Yang Zhang, Haoxuan Li, Peng Wu, Fuli Feng, and Xiangnan He (2023), Causal Recommendation: Progresses and Future Directions. Tutorial on SIGIR 2023.  

[8] Haoxuan Li, Quanyu Dai, Zhenhua Dong, Xiao-Hua Zhou, and Peng Wu* (2023), Multiple Robust Learning for Recommendation. Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI 23, Oral)


[9] Haoxuan Li, Yan Lyu, Chunyuan Zheng, and Peng Wu* (2023), TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations. Proceedings of the 11th International Conference on Learning Representations (ICLR 23)  


[10] Haoxuan Li, Chunyuan Zheng, and Peng Wu* (2023), StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random. Proceedings of the 11th International Conference on Learning Representations (ICLR 23)


[11] Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, and Peng Wu* (2023), Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations. Proceedings of ?the ACM Web Conference 2023 (WWW 23, Best Student Paper Runner-up)


[12] Zhihui Yang#, Shasha Han#, Peng Wu#, Mingyue Wang, Ruoyu Li, Xiaohua Zhou, and Hang Li (2023), Modeling Posttreatment Prognosis of Skin Lesions in Patients with Psoriasis in China. JAMA Network Open. 6(4):e236795.


2022

[1] Peng Wu, Zhiqiang Tan, Wenjie Hu, and Xiao-Hua Zhou (2022), Model-Assisted Inference for Covariate-Specific Treatment Effects with High-dimensional Data. Statistica Sinica.


[2] Peng Wu#, Shasha Han#, Xingwei Tong, and Runze Li (2022), Propensity score regression for causal inference with treatment heterogeneity. Statistica Sinica.


[3] Sihao Ding, Peng Wu*, Fuli Feng, Yitong Wang, Xiangnan He, Yong Liao, and Yongdong Zhang (2022), Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. (KDD 22)


[4] Peng Wu#, Haoxuan Li#, Yuhao Deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, and Xiao-Hua Zhou (2022), On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges. International Joint Conference on Artificial Intelligence. (IJCAI 22)  


[5] Quanyu Dai, Haoxuan Li, Peng Wu*, Zhenhua Dong, Xiao-Hua Zhou*, Rui Zhang, Xiuqiang He, Rui Zhang, and Jie Sun (2022), A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. (KDD 22)


[6] Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, and Xiangnan He (2022), Causal Recommendation: Progresses and Future Directions. Tutorial on WWW.


2021

[1] Peng Wu, Xinyi Xu, Xingwei Tong, Qing Jiang, and Bo Lu (2021), Semi-parametric Estimation for Average Causal Effects using Propensity Score based Spline, Journal of statistical planning and inference. 212, 153-168.


[2] Peng Wu, Xingwei Tong, Yi Wang, Jiajuan Liang, and Xiao-Hua Zhou (2021), Robust Quasi-Oracle Estimation of Average Causal Effects. Biostatistics & Epidemiology. 6(1), 144-163.


[3] Na Xu, Peng Wu, Gang Ma, Qirui Hu, Xiuqing Hu, Ronghua Wu, Yunfeng Wang, Hanlie Xu, Lin Chen, and Peng Zhang (2021), In-flight spectral response function retrieval of a multi-spectral radiometer based on the functional data analysis technique. IEEE Transactions on Geoscience and Remote Sensing. 60, 1-10.


[4] Yi Wang, Peng Wu, Xingwei Tong, and Jianguo Sun (2021), A Weighted Method for the Exclusive Hypotheses Test with Application to Typhoon Data, Canadian Journal of Statistics. 49(4):1258-1272.


2020年及之前

[1] Peng Wu, Baosheng Liang, Yifan Xia, and Xingwei Tong (2020), Predicting Disease Risk by Matching Quantile estimation for Censored Data, Mathematical Biosciences and Engineering. 17(5):4544-4562.


[2] Peng Wu, Qirui Hu, Xingwei Tong, and Min Wu (2020), Learning Causal Effect Using Machine Learning with Application to China's Typhoon. Acta Mathematicae Applicatae Sinica, English Series. 36(3): 702-713.


[3] Baosheng Liang, Peng Wu, Xingwei Tong, and Yanping Qiu (2020), Regression and Subgroup Detection for Heterogeneous Samples. Computational Statistics. 35, 1853-1878.


[4] 侯静惟, 方伟华, 程锰, 叶妍婷, 吴鹏, 韩轶男 (2019), 基于Copula函数的海南热带气旋风雨联合概率特征剖析, 自然灾害学报. 28(3):54-64.


[5] Wanmei Mo, Weihua Fang, Xinze Li, Peng Wu, and Xingwei Tong (2016), Development of vulnerability curves to typhoon hazards based on insurance policy and claim dataset, EGU General Assembly Conference Abstracts. 18, EPSC2016-3360.


R软件包

[1] Wu P., Hu W., Deng Y. and Zhou X-H. (2021) CSTE, https://CRAN.R-project.org/package=CSTE

[2] Yang Y., Wu P., Gai X., Qiu Y. and Zhou X-H. (2021) BrainCon, https://CRAN.R-project.org/package=BrainCon



来源:数学与统计学院    宣布日期:2022-10-21    阅读次数:
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