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 models, causal factor analysis, and causal representation learning.
Instrumental Variable Model, especially in discovering valid IVs from observational data.
I'm looking for students. Welcome to contact me if you are interested!
Publications (* Equal Contribution) [Google Scholar Link]
Preprints-
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. arXiv:2308.06718, 2023.
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Kang Shuai, Shanshan Luo, Yue Zhang, Feng Xie, and Yangbo He. Identification and estimation of causal effects using non-Gaussianity and auxiliary covariates. arXiv:2304.14895, 2023.
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Zhengming Chen, Jie Qiao, Feng Xie, Ruichu Cai, Zhifeng Hao, Keli Zhang. Testing Conditional Independence Between Latent Variables by Independence Residuals. IEEE Transactions on Neural Networks and Learning Systems., 2024.
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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. The Twelfth International Conference on Learning Representations (ICLR 2024), 2024.
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Lingjing Kong, Biwei Huang, Feng Xie, Eric Xing, Yuejie Chi, Kun Zhang. Identification of Nonlinear Latent Hierarchical Models. Advances in Neural Information Processing Systems (NeurIPS), 2023.
Zhengming Chen*, Feng Xie*, Jie Qiao, Zhifeng Hao, and Ruichu Cai. Some General Identification Results for Linear Latent Hierarchical Causal Structure. International Joint Conference on Artificial Intelligence (IJCAI), 2023.
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.
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]
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.
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]
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, 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.
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)
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] [Matlab code]
Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Libo Huang, and Shohei Shimizu. Nonlinear causal discovery for high-dimensional deterministic data. IEEE Transactions on Neural Networks and Learning, 2021, 34(5), 2234 - 2245. [pdf]
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 [pdf]
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. [pdf]
Feng Xie, Ruichu Cai, Yan Zeng, and Zhifeng Hao. Causal Discovery of Linear Non-Gaussian Acyclic Model with Small Samples. International Conference on Intelligent Science and Big Data Engineering (IScIDE 2019), 2019. [pdf]
Ruichu Cai, Feng Xie, Wei Chen, Zhifeng Hao. An efficient kurtosis-based causal discovery method for linear non-Gaussian acyclic data. IEEE/ACM 25th International Symposium on Quality of Service (IWQoS), 2017. [pdf]
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]
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