Ruichu Cai
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个人简介
蔡瑞初，教授、博士生导师、数据挖掘与信息检索实验室主任、国家优秀青年基金获得者。 2010年于华南理工大学获得工学博士学位，并进入广东工业大学工作；2015年并被评为教授、博士生导师；曾先后到新加坡国立大学、UIUC高等数字科学研究中心访问学习。 蔡教授专注于因果关系发现与因果性学习、深度学习等领域的理论与应用研究。在上述领域先后主持国家优秀青年基金、科技部”科技创新2030“重大项目、省杰出青年基金、省特支计划等项目；在因果关系发现、因果性学习方面开展了系列有益探索，在ICML、NIPS、AAAI、IJCAI等领域重要会议和TNNLS、TKDE等国际著名期刊发表论文100余篇；协助华为、网易、腾讯、滴滴、唯品会、南方电网、南方通讯建设等企业解决了因果故障定位、因果决策优化、因果个性推荐等应用难题，取得了良好的经济和社会价值；获得省科学技术一等奖（第三完成人）、国家发明专利奖优秀奖（第三完成人）等奖项；指导学生获得NeurIPS 2019解耦学习算法大赛第一名、亚太因果推理大会推理大赛第一名、“互联网+”全国决赛金奖等奖项；担任Neural Networks杂志Action Editor、NeurIPS、ICML等会议的Area Chair，IJCAI、AAAI等会议的SPC等。
教育经历
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华南理工大学
中国, 广州 - 应用数学学士和计算机科学博士学位 2001 年 9 月 - 2010 年 7 月
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新加坡国立大学
新加坡 - 计算机应用技术(联合培养)工学博士学位 2007 年 9 月 - 2009年 9 月
相关链接
Portfolio: https://sites.google.com/site/cairuichu
Github: https://github.com/DMIRLAB-Group
Causal Learn: causal-learn: Causal Discovery in Python
工作经历
广东工业大学
- 讲师，副教授，正教授 2010 年 7 月 - 现在
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Advanced Digital Sciences Center, a center of University of Illinois at Singapore
- 客座高级研究员 2013 年 7 月 - 2014 年 7 月
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网易
- 数据科学家(兼职顾问) 2009 年 11 月 - 2013 年 6 月
研究兴趣
- 因果关系发现与因果性学习
- 深度学习，自然语言处理等等
News
- 2024/5/4， 一篇文章"A survey on causal reinforcement learning"被TNNLS录用！
- 2024/5/2， 团队(DMIR实验室)的4篇论文被 ICML 2024 录用！
- 2024/4/23，一篇文章"Long-term Causal Effects Estimation via Latent Surrogates Representation Learning"被Neural Networks录用！
- 2024/4/20， 应邀担任 Fundamental Research 杂志的青年编委！
- 2024/4/17， 团队(DMIR实验室)的2篇文章被 IJCAI 2024 录用！
- 2023/12/10， 团队(DMIR实验室)的8篇论文被AAAI 2024录用！
- 2023/10/19， 1篇文章"On the role of entropy-based loss for learning causal structures with continuous optimization"被TNNLS录用！
- 2023/10/11， 应邀担任《计算机工程》青年编委！
- 2023/10/03， 应邀担任 Neural Networks 杂志的执行编辑（Action Editor）！
- 2023/09/10， 1篇文章"REST: Debiased Social Recommendation via Reconstructing Exposure Strategies"被TKDD录用！
- 2023/08/06， 1篇文章"Transferable time-series forecasting under causal conditional shift"被TPAMI录用！
科研项目
- 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
主要论文
因果关系发现- 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, Early Access:1 - 15
- 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. TNNLS2024
- 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, 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
- 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
主要荣誉
- 国家自然科学基金优秀青年科学基金获得者 - 2022 年 1 月
- 中国专利优秀奖 - 2019 年 10 月
- 广东省自然科学一等奖 - 2016 年 4 月
- 广东省自然科学基金优秀青年科学基金获得者 - 2014 年 5 月
- 广东省自然科学二等奖 - 2014 年 4 月
学术兼职
- 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.