謝峰檢視原始碼討論檢視歷史
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謝峰，男，北京工商大學副教授。
人物簡歷
2010.9-2014.6 廣東工業大學 信息與計算科學 理學學士
2014.9-2017.6 廣東工業大學 數學 理學碩士
2017.9-2020.6 廣東工業大學 計算機應用工程 工學博士
2018.9-2018.11 & 2019.7-2020.5 卡內基梅隆大學 (美國) 訪問學生
2020.7-2020.6 北京大學 數學科學學院 博雅博士後
2022.6-至今 北京工商大學 數學與統計學院 副教授
研究興趣
學術成果
論文
-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. (人工智能頂會, CCF A類)
· 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. (人工智能頂會, CCF A類)
· 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. (人工智能頂會, CCF A類)
· 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. (人工智能頂會, CCF A類)
· 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. (人工智能頂會, CCF A類, 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. (人工智能頂會, CCF A類)
-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)
學術兼職
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.[1]