Ruichu Cai 蔡瑞初
Professor |
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Biography
I am a Professor in the School of Computer and the director of the Data mining and Information Retrieval Laboratory, Guangdong University of Technology. I received my B.S. degree in Applied Mathematics and Ph.D. degree in Computer Science from South China University of Technology in 2005 and 2010, respectively. My research interests cover various topics, including causality, deep learning, and their applications. I am a recipient of the National Science Fund for Excellent Young Scholars, Natural Science Award of Guangdong, and so on awards. I have served as the action editor of Neural Networks, the area chair of ICML 2022-2024, NeurIPS 2022-2024, ICLR 2024, UAI 2021-2024 and so on. I am now a senior member of CCF and IEEE.
Education
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South China University of Technology Guangzhou, China
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Bachelor of Applied Mathematics and Ph.D. of Computer Science September 2001 - July 2010
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National University of Singapore Singapore
- Visiting Students of Computer Science September 2007 - September 2009
Source
Portfolio: https://sites.google.com/site/cairuichu
Github: https://github.com/DMIRLAB-Group
Causal Learn: causal-learn: Causal Discovery in Python
Experience
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Guangdong University of Technology
- Lecturer, Associate Professor, Full Professor July 2010 - Present
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Advanced Digital Sciences Center, a center of University of Illinois at Singapore
- Visiting Senior Researcher July 2013 - July 2014
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Netease
- Data Scientist (Part-time consultant) November 2009 - June 2013
Research Interests
- Causal discovery and causality-related learning
- Deep learning, natural language processing and so on
News
- 2024/9/26, 2 papers of my group (DMIR) are aceepted by NeurIPS 2024!
- 2024/5/16, one paper 'S2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis' has been accepted by ACL!
- 2024/5/4, one paper 'A survey on causal reinforcement learning' has been accepted by TNNLS!
- 2024/5/2, 4 papers of my group (DMIR) are aceepted by ICML 2024!
- 2024/4/23, one paper 'Long-term Causal Effects Estimation via Latent Surrogates Representation Learning' has been accepted by Neural Networks!
- 2024/4/20, I has been invited to serve as the youth editor of Fundamental Research!
- 2024/4/17, 2 papers have been accepted by IJCAI 2024!
- 2023/12/10, 8 papers of my group (DMIR) are accepted by AAAI 2024, congratulations!
- 2023/10/18, one paper 'On the role of entropy-based loss for learning causal structures with continuous optimization' has been accepted by TNNLS!
- 2023/10/03， I has been invited to serve as action editor of Neural Networks from 2024 to 2026!
- 2023/09/10， one paper 'REST: Debiased Social Recommendation via Reconstructing Exposure Strategies' is accepted by TKDD!
- 2023/08/06， one paper 'Transferable time-series forecasting under causal conditional shift' is accepted by TPAMI!
Selected Projects
- National Key R&D Program of China, Research on Causal Inference and Decision Theories, 2021ZD0111500, January 2022-December 2025
- National Science Fund for Excellent Young Scholars, Causal Inference Theory and Methods for High-Dimensional Heterogeneous Data, 62122022, January 2022-December 2024
- National Natural Science Foundation of China, Research on Causal Mechanism and Methods of Nonstationary Social Network User Behaviors, 61876043, January 2019-December 2022
- National Natural Science Foundation of China, Research on Causal Mechanism and Methods of Nonstationary Social Network User Behaviors, 61876043
- Causal Discovery on Spatial-Temporal Data with Latent Confounders (October 2021-October 2022), supported by Huawei
- Causal Discovery on High Dimensional Alarm Data and Applications (September 2019- July 2021), supported by Huawei
- Long-term Causal-effect Analysis based on Short-term Surrogates (January 2022-December 2022), supported by Didi Chuxing
Selected Publications
Causal Discovery- 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
- Wei Chen, Xiaokai Huang, Zijian Li, Ruichu Cai*, Zhiyi Huang, Zhifeng Hao. Individual Causal Structure Learning from Population Data. IJCAI 2024
- Wei Chen, Zhiyi Huang, Ruichu Cai*, Zhifeng Hao, Kun Zhang. Identification of Causal Structure with Latent Variables based on Higher Order Cumulants. 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
- 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
- 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, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang. Causal Discovery with Latent Confounders Based on Higher-Order Cumulants. ICML 2023
- 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
- Wei Chen, Ruichu Cai*, Kun Zhang, Zhifeng Hao. Causal Discovery in Linear Non-Gaussian Acyclic Model with Multiple Latent Confounders. IEEE Transactions on Neural Networks and Learning Systems, 2021
- Feng Xie*, Ruichu Cai*, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang*. Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs. NeurIPS 2020
- Feng Xie, Ruichu Cai*, Yan Zeng, Jiantao Gao, 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, 2020, 31(5): 1667 - 1680
- 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, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang. Triad Constraints for Learning Causal Structure of Latent Variables. NeurIPS 2019
- Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Discovery with Cascade Nonlinear Additive Noise Model. IJCAI 2019
- Ruichu Cai, Zhenjie Zhang, Zhifeng Hao, Marianne Winslett. Sophisticated Merging over Random Partitions: A Scalable and Robust Causal Discovery Approach. IEEE Transactions on Neural Networks and Learning Systems, 2018:29(8) : 3623 - 3635
- 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
- Ruichu Cai, Zhenjie Zhang, Zhifeng Hao. SADA: A General Framework to Support Robust Causation Discovery. ICML 2013
- 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
- Ruichu Cai, Siyu Wu, Jie Qiao, Zhifeng Hao, Keli Zhang, Xi Zhang. THPs: Topological Hawkes Processes for Learning Causal Structure on Event Sequences. IEEE Transactions on Neural Networks and Learning Systems. 2022, 35(1):479 - 493
- Ruichu Cai, Zhenjie Zhang, Zhifeng Hao, Marianne Winslett. Understanding Social Causalities Behind Human Action Sequences. IEEE Transactions on Neural Networks and Learning Systems. 2017, 28(8):1801-1813
- Yan Zeng, Ruichu Cai, Fuchun Sun, Libo Huang, Zhifeng Hao. A survey on causal reinforcement learning. TNNLS 2024
- Xuexin Chen, Ruichu Cai*, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, Jose Miguel Hernandez-Lobato. Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation.ICML 2023
- 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
- Ruichu Cai*, Fengzhu Wu, Zijian Li, Jie Qiao, Wei Chen, Yuexing Hao, Hao Gu. REST: Debiased Social Recommendation via Reconstructing Exposure Strategies. TKDD 2023
- Zijian Li, Ruichu Cai*, Tom Fu, Zhifeng Hao, Kun Zhang. Transferable time-series forecasting under causal conditional shift. TPAMI 2023
- Ruichu Cai, Jiawei Chen, Zijian Li, Wen Chen, Keli Zhang, Junjian Ye, Zhuozhang Li, Xiaoyan Yang, Zhenjie Zhang. Time Series Domain Adaptation via Sparse Associative Structure Alignment. AAAI 2021
- Ruichu Cai, Jiahao Li, Zhenjie Zhang, Xiaoyan Yang, Zhifeng Hao. DACH: Domain Adaptation without Domain Information. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(12):5055-5067
- Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao. Learning Disentangled Semantic Representation for Domain Adaptation. IJCAI 2019
- Bingfeng Chen, Qihan Ouyang, Yongqi Luo, Boyan Xu*, Ruichu Cai, Zhifeng Hao. S2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis. ACL 2024
- Jiahao Li, Ruichu Cai*, Yuguang Yan. Combinatorial Routing for Neural Trees. IJCAI 2024
- Yuguang Yan, Zhihao Xu, Canlin Yang, Jie Zhang, Ruichu Cai*, Michael Kwok-Po Ng. An Optimal Transport View for Subspace Clustering and Spectral Clustering. AAAI 2024
- Yuguang Yan, Yuanlin Chen, Shibo Wang, Hanrui Wu, Ruichu Cai*. Hypergraph Joint Representation Learning for Hypervertices and Hyperedges via Cross Expansion. AAAI 2024
- Xuexin Chen, Ruichu Cai*, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao. Motif Graph Neural Network. IEEE Transactions on Neural Networks and Learning Systems. 2023, Early Access
- Zhifeng Hao, Junbin Chen, Wen Wen, Biao Wu, Ruichu Cai. A selection-pattern-aware recommendation model with colored-motif attention network. Neurocomputing, 2023: 538: 126-178
- Ruichu Cai, Jinjie Yuan, Boyan Xu, Zhifeng Hao. SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL. NeurIPS 2021
- Ruichu Cai, Hao Zhang, Wen Liu, Shenghua Gao, Zhifeng Hao. Appearance-Motion Memory Consistency Network for Video Anomaly Detection. AAAI 2021
- Ruichu Cai, Zhihao Liang, Boyan Xu, zijian li, Yao Chen and Yuexing Hao. TAG: Type Auxiliary Guiding for Code Comment Generation. ACL 2020
- Ruichu Cai, Xuexin Chen, Yuan Fang, Min Wu, Yuexing Hao. Dual-Dropout Graph Convolutional Network for Predicting Synthetic Lethality in Human Cancers. Bioinformatics, 2020, 36(16):4458-4465
- Ruichu Cai, Boyan Xu, Xiaoyan Yang, Zhengjie Zhang, Zijian Li, Zhihao Liang. An Encoder-Decoder Framework Translating Natural Language to Database Queries. IJCAI 2018
- Yuguang Yan, Hao Zhou, Zeqin Yang, Weilin Chen, Ruichu Cai*, Zhifeng Hao. Reducing Balancing Error for Causal Inference via Optimal Transport. ICML 2024
- Feng Xie, Zhengming Chen, Shanshan Luo, Wang Miao, Ruichu Cai, Zhi Geng. Automating the Selection of Proxy Variables of Unmeasured Confounders. ICML 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
- Ruichu Cai*, Weilin Chen, Zeqin Yang, Shu Wan, Chen Zheng, Xiaoqing Yang, Jiecheng Guo. Long-term Causal Effects Estimation via Latent Surrogates Representation Learning. Neural Networks, 2024
- Yuguang Yan, Zeqin Yang, Weilin Chen, Ruichu Cai*, Zhifeng Hao, Michael Kwok-Po Ng. Exploiting Geometry for Treatment Effect Estimation via Optimal Transport. AAAI 2024
- Ruichu Cai, Zeqin Yang, Weilin Chen, Yuguang Yan, Zhifeng Hao. Generalization Bound for Estimating Causal Effects from Observational Network Data. CIKM 2023
- Ruichu Cai, Zijie Lu, Li Wang, Zhenjie Zhang. DITIR: Distributed Index for High Throughput Trajectory Insertion and Real-time Temporal Range Query. PVLDB 2017
- Ruichu Cai, Mei Liu, Yong Hu , Brittany L. Melton, Michael E. Matheny,Hua Xu, Lian Duan, Lemuel R. Waitman. Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports. Artificial Intelligence in Medicine 76 (2017) 7-15
- Ruichu Cai, Zhenjie Zhang, Srinivasan Parthasarathy, Anthony K. H. Tung, Zhifeng Hao, Wen Zhang. Multi-Domain Manifold Learning for Drug-Target Interaction Prediction. SDM 2016
- Ruichu Cai, Zhifeng Hao, Marianne Winslett, Xiaokui Xiao, Yang Yin, Zhenjie Zhang, Shuigeng Zhou. Deterministic Identification of Specific Individuals from GWAS Results[J]. Bioinformatics, 2015, 31(11): 1701-1707
- Yong Hu, Xiangzhou Zhang, EWT Ngai, Ruichu Cai, Mei Liu. Software project risk analysis using Bayesian networks with causality constraints. Decision Support Systems, 2013, 56: 439-449
- Ruichu Cai, Zhenjie Zhang, Zhifeng Hao. Causal Gene Identification Using Combinatorial V-Structure Search, Neural Networks. 2013;43:63-71
- Mei Liu, Ruichu Cai (Co-First Author), Yong Hu, ME Matheny, Jianchuan Sun, Jun Hu. Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning[J]. JAMIA, 2013, 21(2):245-51
- Ruichu Cai, Tung K.H. Anthony, Zhifeng Hao, Zhenjie Zhang. What is Unequal among the Equals? Ranking Equivalent Rules from Gene Expression Data. IEEE Transactions on Knowledge and Data Engineering, 2011;23(11):1735-1747
Honors and Awards
- National Science Fund for Excellent Young Scholars - January, 2022
- The China Patent Awards Excellence Award - October, 2019
- 1st Class, Natural Science Award of Guangdong - April, 2016
- National Science Fund for Excellent Young Scholars of Guangdong - May, 2014
- 2nd Class, Natural Science Award of Guangdong - April, 2014
Services
- Action Editor: Neural Networks
- The Youth Editor: Fundamental Research
- Area Chair: ICML2022, ICML2023, ICML2024, NeurIPS2022, NeurIPS2023, NeurIPS2024, ICLR2024, UAI2021, UAI2022, UAI2023
- Senior PC: AAAI 2019-2022, IJCAI 2019-2021
- PC: AAAI 2015-2019, IJCAI 2018-2019, NIPS 2016-2018, ICML 2015-2018, AISTATS 2016-2019, ICLR 2018-2019
- Associate Editor: Frontiers in Bioinformatics
- Reviewer: TNNLS, TPAMI, TKDE, TIST, Neural Network, Pattern Recognition, Bioinformatics, Neurocomputing, Information Sciences, National Science Review, Science China-Information Sciences and so on.