Jie Qiao 乔杰
Postdoctoral Researcher |
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Biography
I am a postdoctoral researcher in the School of Computers at Guangdong University of Technology. I received my B.S. degree in Statistic and Ph.D. degree in Computer Science from Guangdong University of Technology, where I was very fortunate to be advised by Prof. Ruichu Cai. My research interests cover various topics, including causality, causality-inspired machine learning. I have served as PC member of ICML(2022-2024), NeurIPS (2022-2024), ICLR (2024-2025), AAAI (2021-2024), IJCAI (2023-2024), UAI (2022-2024), AISTATS (2025), etc.
Education
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Guangdong University of Technology Guangzhou, China
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Bachelor of Statistic and Ph.D. of Computer Science September 2012 - December 2021
Experience
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Guangdong University of Technology Guangzhou, China
- Postdoctoral researcher July 2022 - Present
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The University of Hong Kong Hong Kong
- Visiting Scholar May 2024 - April 2025
Research Interests
- Causal discovery and causality-related learning
News
- 2024/9/26, 2 papers are aceepted by NeurIPS 2024!
- 2023/12/10, 4 papers are accepted by AAAI 2024!
- 2023/10/18, one paper 'On the role of entropy-based loss for learning causal structures with continuous optimization' has been accepted by TNNLS!
Selected Projects
- National Natural Science Foundation of China, Research on Causal Discovery from Partially Obseved Event Sequences, 6240070666, January 2025-December 2027
Selected Publications
- Yu Xiang*, Jie Qiao*, Zhefeng Liang, Zihuai Zeng, Ruichu Cai, Zhifeng Hao. On the Identifiability of Poisson Branching Structural Causal Model Using Probability. NeurIPS 2024
- Zhengming Chen, Ruichu Cai, Feng Xie, Jie Qiao, Anpeng Wu, Zijian Li, Zhifeng Hao, Kun Zhang* Learning Discrete Latent Variable Structures with Tensor Rank Conditions. NeurIPS 2024
- Jie Qiao, Zhengming Chen, Jianhua Yu, Ruichu Cai, Zhifeng Hao. Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model. AAAI 2024
- Jie Qiao*, Yu Xiang*, Zhengming Chen, Ruichu Cai, Zhifeng Hao. Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis. AAAI 2024
- Ruichu Cai, Yuxuan Zhu, Jie Qiao†, Zefeng Liang, Furui Liu, Zhifeng Hao. Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples. AAAI 2024
- Yuequn Liu, Ruichu Cai, Wei Chen, Jie Qiao, Yuguang Yan, Zijian Li, Keli Zhang, Zhifeng Hao. TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences. AAAI 2024
- Weilin Chen, Ruichu Cai, Zeqin Yang, Jie Qiao, Yuguang Yan, Zijian Li, Zhifeng Hao. Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning. ICML 2024
- Zhengming Chen, Jie Qiao, Feng Xie, Ruichu Cai, Zhifeng Hao, Keli Zhang. Testing Conditional Independence Between Latent Variables by Independence Residuals. TNNLS. 2024.
- Jie Qiao, Ruichu Cai, Siyu Wu, Yu Xiang, Kun Zhang, Zhifeng Hao. Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event. IJCAI 2023
- Weilin Chen*, Jie Qiao*, Ruichu Cai, Zhifeng Hao. On the role of entropy-based loss for learning causal structures with continuous optimization. TNNLS 2023
- Ruichu Cai, Fengzhu Wu, Zijian Li, Jie Qiao, Wei Chen, Yuexing Hao, Hao Gu. REST: Debiased Social Recommendation via Reconstructing Exposure Strategies. TKDD 2023
- Jie Qiao, Yiming Bai, Ruichu Cai, and Zhifeng Hao. Causal discovery from multi-domain data using the independence of modularities. Neural Computing and Applications, 2022, 34(3): 1939–1949.
- Zhengming Chen*, Feng Xie*, Jie Qiao*, Zhifeng Hao, Kun Zhang, Ruichu Cai. Identification of Linear Latent Variable Model with Arbitrary Distribution. AAAI 2022
- Yuequn Liu*, Wenhui Zhu*, Jie Qiao*, et.al. Causal Alignment Based Fault Root Causes Localization for Wireless Network. ICASSP 2022
- Ruichu Cai, Siyu Wu, Jie Qiao, Zhifeng Hao, Keli Zhang, Xi Zhang. THPs: Topological Hawkes Processes for Learning Causal Structure on Event Sequences. TNNLS 2022
- Jie Qiao, Ruichu Cai, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models. ACM Transactions on Intelligent Systems and Technology (TIST), 2021: 12(6): 1-28
- Jie Qiao, Yiming Bai, Ruichu Cai, and Zhifeng Hao. Learning causal structures using hidden compact representation. Neurocomputing, 2021, 463: 328-333.
- Ruichu Cai, Jincheng Ye, Jie Qiao, Huiyuan Fu, Zhifeng Hao. FOM: Fourth-Order Moment based Causal Direction Identification on the Heteroscedastic Data. Neural Networks, 2020, 124:193-201
- Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Discovery with Cascade Nonlinear Additive Noise Model. IJCAI 2019
- Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao. Learning Disentangled Semantic Representation for Domain Adaptation. IJCAI 2019
- Ruichu Cai, Jie Qiao, Zhenjie Zhang , et al. SELF: Structural Equational Likelihood Framework for Causal Discovery. AAAI 2018
- Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Inference from Discrete Data using Hidden Compact Representation. NeurIPS 2018
Services
- Reviewer for Conferences: ICML(2022-2024), NeurIPS (2022-2024), ICLR (2024-2025), AAAI (2021-2024), IJCAI (2023-2024), UAI (2022-2024), AISTATS (2025)
- Reviewer for journals: Journal of Machine Learning Research, Neural Network, Pattern Recognition, Information Sciences, Neurocomputing.