应用统计系

副教授

谢峰

  • 邮箱:fengxie@btbu.edu.cn

    地址:北京市房山区北京工商大学良乡主校区东区开云·体育(中国)官方网站数统楼209室

    个人简介

    谢峰,男,河北邯郸人副教授,硕士生导师。广东工业大学工学博士,美国卡耐基梅隆大学联合培养博士北京大学数学科学学院博雅博士后。现任应用统计系系主任,兼任全国工业统计学教学研究会理事,中国现场统计研究会因果推断分会理事。

    研究兴趣

    主要研究因果推断、人工智能等领域的统计理论与应用研究,尤其是因果发现的机理,隐变量的因果表达学习以及因果发现算法在社会学、经济学中的应用近五年研究成果发表于研究成果已发表于多个国际顶级学术会议(如ICMLNeurIPSICLRAAAIIJCAI)和重要学术期刊(如JMLRIEEE Transactions on NNLSNeurocomputing)。

    主讲课程

    本科生课程《机器学习》、《统计计算》

    学习经历

    20109-20146月,广东工业大学 信息与计算科学专业, 理学学士;

    20149-20176月,广东工业大学 数学专业, 理学硕士;

    20197-20205月,美国卡内基梅隆大学 联合培养博士

    20179-20206月,广东工业大学 计算机应用工程专业, 学博士

    工作经历

    20207-20226月,北京大学 数学科学学院 博雅博士后;

    20227月至今,北京工商大学 开云·体育(中国)官方网站, 副教授

    主要获奖荣誉

    2023年,北京工商大学工会积极分子

    主要科研项目

    主持国家自然科学基金项目1项、中国博士后科学基金项目1项。主要有:

    1.国家自然科学基金青年项目面向离散数据的隐变量间因果关系推断理论与方法研究202401-202612月,30,主持人;

    2.中国博士后科学基金面上项目含有隐变量数据上的因果关系推断理论研究及其应用202011-202206月,8,主持人

    主要学术成果

    发表论文20,主要有:

    1. Kang Shuai, Shanshan Luo, Yue Zhang, Feng Xie, and Yangbo He. Identification and estimation of causal effects using non-Gaussianity and auxiliary covariates. To appear in Statistica Sinica, 2024.

    2. Feng Xie*, Biwei Huang*, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, and Kun Zhang. Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. To appear in JMLR, 2024. (人工智能顶, CCF A)

    3. Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, and Zhi Geng. Local Causal Structure Learning in the Presence of Latent Variables. ICML, Vienna, Austria, 2024. (人工智能顶会, CCF A)

    4. Feng Xie, Zhengming Chen, Shanshan Luo, Wang Miao, Ruichu Cai, Zhi Geng.

    Automating the Selection of Proxy Variables of Unmeasured Confounders. ICML, Vienna, Austria, 2024. (Spotlight) (人工智能顶会, CCF A)

    5. Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, and Yan Zeng.

    Policy Learning for Balancing Short-Term and Long-Term Rewards. ICML, Vienna, Austria, 2024. (人工智能顶会, CCF A)

    6. Zhengming Chen, Jie Qiao, Feng Xie, Ruichu Cai, Zhifeng Hao, Keli Zhang. Testing Conditional Independence Between Latent Variables by Independence Residuals. IEEE Transactions on NNLS, 2024.

    7. Songyao Jin, Feng Xie, Guangyi Chen, Biwei Huang, Zhengming Chen, Xinshuai Dong, Kun Zhang. Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability.ICLR, 2024.

    8. Lingjing Kong, Biwei Huang, Feng Xie, Eric Xing, Yuejie Chi, Kun Zhang. Identification of Nonlinear Latent Hierarchical Models. NeurIPS, 2023. (人工智能顶会, CCF A)

    9. Zhengming Chen*, Feng Xie*, Jie Qiao, Zhifeng Hao, and Ruichu Cai. Some General Identification Results for Linear Latent Hierarchical Causal Structure. IJCAI, 2023. (人工智能顶会, CCF A)

    10. Feng Xie, Yan Zeng, Zhengming Chen, Yangbo He, Zhi Geng, and Kun Zhang. Causal Discovery of 1-Factor Measurement Models in Linear Latent Variable Models with Arbitrary Noise Distributions. Neurocomputing, 2023.

    11. Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, and Kun Zhang. Identification of Linear Non-Gaussian Latent Hierarchical Structure. ICML, Baltimore, Maryland USA, 2022. (Spotlight) (人工智能顶会, CCF A)

    12. Biwei Huang, Charles Low, Feng Xie, Clark Glymour, Kun Zhang. Latent Hierarchical Causal Structure Discovery with Rank Constraints. NeurIPS, 2022. (人工智能顶会, CCF A)

    13. Z. Chen*, Feng Xie*, Jie Qiao*, Zhifeng Hao, Kun Zhang, and Ruichu Cai. Identification of Linear Latent Variable Model with Arbitrary Distribution. AAAI, Vancouver, CANADA, 2022. (人工智能顶会, CCF A)

    14. Feng Xie, Yangbo He, Zhi Geng, Zhengming Chen, Ru Hou, and Kun Zhang. Testability of Instrument Validity in Linear non-Gaussian Acyclic Causal Models. Entropy, 2022, 24(4), 512.

    15. Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Libo Huang, and Shohei Shimizu.

    Nonlinear causal discovery for high-dimensional deterministic data. IEEE Transactions on NNLS, 2021, 34(5), 2234 - 2245.

    16. Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, and Zhifeng Hao. Causal discovery with multi-domain LiNGAM for latent factors. IJCAI, Montreal-themed Virtual Reality, 2021. (人工智能顶会, CCF A)

    17. Feng Xie, Ruichu Cai, Yan Zeng, and Zhifeng Hao. An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models with IID Noise Variables. IEEE Transactions on NNLS, 2020, 31(5): 1667-1680.

    18. Feng Xie*, Ruichu Cai*, Biwei Huang, Clark Glymour, Zhifeng Hao, and Kun Zhang*. Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. NeurIPS, Virtual Conference, 2020. (Spotlight) (人工智能顶会, CCF A)

    19. Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Liang Ou, and Ruihui Huang. A causal discovery algorithm based on the prior selection of leaf nodes. Neural Networks, 2020, 124, 130-145.

    20. Wei Chen, Ruichu Cai, Zhifeng Hao, Chang Yuan, and Feng Xie. Mining hidden non-redundant causal relationships in online social networks. Neural Computing and Applications, 2020, 32, 6913-6923.

    21. Ruichu Cai*, Feng Xie*, Clark Glymour, Zhifeng Hao, and Kun Zhang. Triad Constraints for Causal Discovery in the Presence of Latent Variables. NeurIPS, Vancouver, CANADA, 2019. (人工智能顶会, CCF A)

    22. Feng Xie, Ruichu Cai, Yan Zeng, and Zhifeng Hao. Causal Discovery of Linear Non-Gaussian Acyclic Model with Small Samples. IScIDE 2019, 2019.

    23. Ruichu Cai, Feng Xie, Wei Chen, Zhifeng Hao. An efficient kurtosis-based causal discovery method for linear non-Gaussian acyclic data. IWQoS, 2017.