Feng Xie
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Biography
I am currently an associate professor in the Department of Applied Statistics at Beijing Technology and Business University. Before joining BTBU, I did postdoctoral research in the Department of Probability and Statistics at Peking University from 2020 to 2022, working with Prof Zhi Geng and Prof Yangbo He. I obtained my PhD in the School of Computer Science at Guangdong University of Technology (2017 - 2020), supervised by Prof Ruichu Cai and co-supervised by Prof Kun Zhang (Carnegie Mellon University). I got my Master’s degree in the School of Mathematics and Statistics at Guangdong University of Technology (2014 - 2017), supervised by Prof Zhifeng Hao. From 2019 - 2020, I was a visiting Ph.D. student in the Department of Philosophy, Carnegie Mellon University.
Research Interests:
Causal Discovery, especially in latent variable model, causal factor analysis, and causal representation learning
Instrumental Variable Model, especiallg in discovery valid IV from observational data
I'm looking for students. Welcome to contact me if you are interested!
Selected Publications (* Equal Contribution)
Latent Causal Structure/Causal Factor Analysis/Causal Representation LearningFeng 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.
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. [pdf][code]
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. [pdf][Matlab code][Python code] (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. [pdf] [code]
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. [pdf]
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
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.
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. [pdf](JCR Q2, IF 2.524)