数学与统计学院

吴鹏


电话:(86)13051321298

邮箱:pengwu@btbu.edu.cn

通讯地址:北京市良乡高教园区obao欧宝娱乐 -数学与统计学院楼209室

研究兴趣:因果推断, 推荐系统, 机器学习


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

2022.7-2022.7,北京大学,北京国际数学研究中心,博士后

2022.7至今,obao欧宝娱乐 ,数学与统计学院,副教授

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管理研究会理事


科研论文(*corresponding author, #contributed equally)

2023年

[1] 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)


[2] 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)


[3] 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)


[4] 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)


[5] Zhihui Yang#, Shasha Han#, Peng Wu#, Mingyue Wang, Ruoyu Li, Xiaohua Zhou, and Hang Li (2023)

Modeling post-treatment prognosis of skin lesions in psoriasis: A large cohort study in China. JAMA Network Open


2022年

[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. (Online)


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


[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年

[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分析, 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 发表日期:2022-10-21 阅读次数:
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