檢視谢峰的原始碼

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|<center>''' 谢峰  '''<br><img src=" https://www.btbu.edu.cn/images/2022-10/38d82d57cd9e4777bd6c8edc4507bf0f.jpg " width="180"></center><small>[https://www.btbu.edu.cn/szdw/dszy/sssds/sxytjxy/c819b1fd5dde4ad387f861cec9dc286a.htm  北京工商大学]

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'''谢峰'''，男，北京工商大学副教授。

==人物简历==

2010.9-2014.6 广东工业大学 信息与计算科学 理学学士

2014.9-2017.6 广东工业大学 数学 理学硕士

2017.9-2020.6 广东工业大学 计算机应用工程 工学博士

2018.9-2018.11 & 2019.7-2020.5 卡内基梅隆大学 (美国) 访问学生

2020.7-2020.6 北京大学 数学科学学院 博雅博士后

2022.6-至今 北京工商大学 数学与统计学院 副教授
==研究兴趣==
因果推断，[[机器学习]]，[[隐变量结构]]学习，[[隐变量表征]]学习
==学术成果==
=== 论文 ===

-Latent Causal Structure/Causal Factor Analysis/Causal Representation Learning

· Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, and Kun Zhang. Identification of Linear Non-Gaussian Latent Hierarchical Structure. Thirty-ninth International Conference on Machine Learning (ICML), Baltimore, Maryland USA, 2022. (人工智能顶会, CCF A类)

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

· Biwei Huang*, Charles Low*, Feng Xie, Clark Glymour, Kun Zhang. Latent Hierarchical Causal Structure Discovery with Rank Constraints. Advances in Neural Information Processing Systems (NeurIPS), 2022. (人工智能顶会, CCF A类)

· Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, and Zhifeng Hao. Causal discovery with multi-domain LiNGAM for latent factors. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), Montreal-themed Virtual Reality, 2021. (人工智能顶会, CCF A类)

· Feng Xie*, Ruichu Cai*, Biwei Huang, Clark Glymour, Zhifeng Hao, and Kun Zhang*. Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. Advances in Neural Information Processing Systems 33 (NeurIPS), Virtual Conference, 2020. (人工智能顶会, CCF A类, Spotlight)

· 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 Neural Networks and Learning Systems (T-NNLS), 2020, 31(5): 1667-1680. (JCR Q1, IF 11.683)

· Ruichu Cai*, Feng Xie*, Clark Glymour, Zhifeng Hao, and Kun Zhang. Triad Constraints for Causal Discovery in the Presence of Latent Variables. Advances in Neural Information Processing Systems 32 (NeurIPS), Vancouver, CANADA, 2019. (人工智能顶会, CCF A类)

-Instrumental Variables Model

· 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. (JCR Q2, IF 2.524)

==学术兼职==

Reviewer:

Conference-International Conference on Machine Learning (ICML) 2022; Neural Information Processing Systems (NeurIPS) 2021,2022; International Conference on Learning Representations (ICLR 2023); International Conference on Artificial Intelligence and Statistics (AISTATS) 2022, 2023; The Conference on Uncertainty in Artificial Intelligence (UAI) 2022; Causal Learning and Reasoning(CLeaR) 2022; NeurIPS Workshop on Causal Discovery and Causality-Inspired Machine Learning 2020;

Journal-IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2021; ACM Transactions on Knowledge Discovery from Data (TKDD) 2022; Transactions on Machine Learning Research (TMLR) 2022.<ref>[https://www.btbu.edu.cn 北京工商大学]</ref>
==参考资料== 
{{reflist}}

[[Category:教授]]