Research Interests

Causal Discovery, Feature Selection, and Machine Learning

Selected Publications (*PhD student, **Master student)

Preprint:

1. Yuling Li*, Kui Yu, Yuhong Zhang, and Xindong Wu. Learning Relation-Specific Representations for Few-shot Knowledge Graph Completion. arXiv preprint arXiv:2203.11639 (2022). 

2. Kui Yu, Jiuyong Li, and Lin Liu. A Review on Algorithms for Constraint-based Causal Discovery. arXiv:1611.03977 [cs.AI] (2016).

Journal Papers:

1. Xuecheng Ning*, Yujie Wang*, Kui Yu, Jiali Miao, Fuyuan Cao, Jiye Liang. Summary Graph Induced Invariant Learning for Generalizable Graph Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), accepted, 2025.

2. Yongsheng Zhao**, Kui Yu, Guodu Xiang, Xianjie Guo, and Fuyuan Cao. FedECE: Federated Estimation of Causal Effect Based on Causal Graphical Modelling. IEEE Transactions on Artificial Intelligence (TAI), accepted, 2025.

3. Xuechun Jing*, Fuyuan Cao, Kui Yu, Jiye Liang. CM-CaFE: A Clustering Method with Causality-based Feature Embedding.  ACM Transactions on Knowledge Discovery from Data (TKDD), accepted, 2025.

4. Xianjie Guo*, Kui Yu, Lin Liu, Jiuyong Li, Jiye Liang, Fuyuan Cao, and Xindong Wu. Progressive Skeleton Learning for Effective Local-to-Global Causal Structure Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), accepted, 2024.

5. Xuechun Jing*, Fuyuan Cao, Kui Yu, Jiye Liang. FWCEC: An Enhanced Feature Weighting Method via Causal Effect for Clustering. IEEE Transactions on Knowledge and Data Engineering (TKDE), accepted, 2024.

6. Kui Yu, Chen Rong, Hao Wang, Fuyuan Cao, and Jiye Liang. Federated local causal structure learning, SCIENCE CHINA Information Sciences(SCIS), accepted, 2024.

7. Zhaolong Ling, Jingxuan Wu, Yiwen Zhang, Peng Zhou, and Xingyu Wu, Kui Yu, and Xindong Wu. Label-aware Causal Feature Selection. IEEE Transactions on Knowledge and Data Engineering (TKDE), accepted, 2024.

8. Yuwei Wang*, Yuling Li, Kui Yu, and Jing Yang. A semantic structure-based emotion-guided model for emotion-cause pair extraction. Pattern Recognition (PR), accepted, 2024.

9. Fuyuan Cao , Yunxia Wang, Kui Yu , and Jiye Liang. Causal Discovery from Unknown Interventional Datasets over Overlapping Variable Sets. IEEE Transactions on Knowledge and Data Engineering (TKDE), accepted, 2024.

10. Yujie Wang*, Kui Yu, Guodu Xiang, Fuyuan Cao, Jiye Liang. Discovering Causally Invariant Features for Out-of-Distribution Generalization. Pattern Recognition (PR), accepted, 2024.

11. Zhaolong Ling, Jingxuan Wu, Peng zhou, Kui Yu, and Xindong Wu. Fair Feature Selection: A Causal Perspective. ACM Transactions on Knowledge Discovery from Data (TKDD), accepted, 2024.

12. Yuhong Zhang, Zhihao Lin, Chenyang Bu, Kui Yu, Xuegang Hu, and Xindong Wu. A Cognitive Diagnosis Model with Nonlinear Dependence between Students and Exercises. IEEE Transactions on Computational Social Systems (TCSS), accepted, 2024.

13.  Yuhong Zhang, Jianqing Wu, Kui Yu, and Xindong Wu. Diverse Structure-aware Relation Representation in Cross-Lingual Entity Alignment. ACM Transactions on Knowledge Discovery from Data (TKDD), accepted, 2024.

14. Fei Liu, Chenyang Bu, Haotian Zhang, Le Wu, Kui Yu, and Xuegang Hu. FDKT: Towards an interpretable deep knowledge tracing via fuzzy reasoning. ACM Transactions on Information Systems (TOIS), accepted, 2024.

15. Jinghong Xia**, Yuling Li, and Kui Yu. Lgt: long-range graph transformer for early rumor detection. Social Network Analysis and Mining, accepted, 2024.

16. Zhaolong Ling, Jingxuan Wu, Yiwen Zhang, Peng Zhou, Bingbing Jiang, Kui Yu, and Xindong Wu. Causal Feature Selection with Imbalanced Data. IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), accepted, 2024.

17. Guodu Xiang**, Hao Wang, Kui Yu, Xianjie Guo, Fuyuan Cao, and Yukun Song. Bootstrap-based Layer-wise Refining for Causal Structure Learning. IEEE Transactions on Artificial Intelligence (TAI), accepted, 2023. 

18. Kui Yu, Zhaolong Ling, Lin Liu, Peipei Li, Hao Wang, and Jiuyong Li. Feature Selection for Efficient Local-to-Global Bayesian Network Structure Learning. ACM Transactions on Knowledge Discovery from Data (TKDD), accepted, 2023. 

19. Jianli Huang**,Xianjie Guo*, Kui Yu, Fuyuan Cao and Jiye Liang. Towards Privacy-Aware Causal Structure Learning in Federated Setting. IEEE Transactions on Big Data (TBD), accepted, 2023.

20. Yuling Li*, Kui Yu, Yuhong Zhang, Jiye Liang and Xindong Wu. Adaptive Prototype Interaction Network for Few-shot Knowledge Graph Completion. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), accepted, 2023.

21. Shuai Yang, Xianjie Guo, Kui Yu, Xiaoling Huang, Tingting Jiang, Jin He and Lichuang Gu. Causal Feature Selection in the Presence of Sample Selection Bias. ACM Transactions on Intelligent Systems and Technology (TIST), accepted, 2023.

22. Xianjie Guo*, Kui Yu, Lin Liu, Peipei Li, and Jiuyong Li. Adaptive Skeleton Construction for Accurate DAG Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), accepted,2023. 

23. Debo Cheng, Jiuyong Li, Lin Liu, Kui Yu, Thuc Duy Le, and Jixue Liu. Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 10.1109/TNNLS.2023.3262848, 2023. 

24. Shujing Yang, Fuyuan Cao, Kui Yu, Jiye Liang.Learning Causal Chain Graph Structure via Alternate Learning and Double Pruning. IEEE Transactions on Big Data (TBD), 2023, accepted.

25. Yuhong Zhang, Jianqing Wu, Kui Yu, and Xindong Wu. Independent Relation Representation with Line Graph for Cross-Lingual Entity Alignmen. IEEE Transactions on Knowledge and Data Engineering (TKDE), accepted,2022.

26. Zhaolong Ling, Ying Li, Yiwen Zhang, Peng zhou, Kui Yu, and Xindong Wu. A Light Causal Feature Selection Approach to High-Dimensional Data. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022, accepted.

27. Zhaolong Ling, Kui Yu, Yiwen Zhang, Lin Liu, Jiuyong Li. Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning. Pattern Recognition Letters (PRL), 2022, accepted.

28. Zhaolong Ling, Bo Li, Yiwen Zhang, Kui Yu, and Xindong Wu. Causal Feature Selection with Efficient Spouses Discovery. IEEE Transactions on Big Data (TBD), 2022, accepted. 

29. Xianjie Guo*, Kui Yu, Lin Liu, Fuyuan Cao, and Jiuyong Li. Causal Feature Selection with Dual Correction. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022, accepted. 

30. Peipei Li, Yingying Liu, Yang Hu, Yuhong Zhang, Xuegang Hu, and Kui Yu. A Drift-Sensitive Distributed LSTM Method for Short Text Steam Classification. IEEE Transactions on Big Data (TBD), 2022, accepted.

31. Debo Cheng, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu, and  Kui Yu. Sufficient Dimension Reduction for Average Causal Effect Estimation. Data Mining and Knowledge Discovery (DMKD), 2022, accepted.

32. Xianjie Guo*, Kui Yu, Fuyuan Cao, Peipei Li, and Hao Wang. Error-Aware Markov Blanket Learning for Causal Feature Selection. Information Science,  accepted, 2021.

33. Zhaolong Ling, Kui Yu, Lin Liu, Yiwen Zhang, and Xindong Wu. PSL: an Algorithm for Partial Bayesian Network Structure Learning. ACM Transactions on Knowledge Discovery from Data (TKDD), accepted, 2021.

34. Yunxia Wang*,Fuyuan Cao, Kui Yu, and Jiye Liang. Local Causal Discovery in Multiple Manipulated Datasets. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), accepted, 2021.

35. Debo Cheng*, Jiuyong Li, Lin Liu, Kui Yu, Thuc Duy Le, Jixue Liu. Towards precise causal effect estimation from data with hidden variables. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), accepted, 2021.

36. Kui Yu, Yajing Yang, and Wei Ding. Causal Feature Selection with Missing Data.  ACM Transactions on Knowledge Discovery from Data (TKDD), accepted, 2021.

37. Shuai Yang*, Kui Yu, Fuyuan Cao, Lin Liu, Hao Wang, and Jiuyong Li. Learning Causal Representations for Robust Domain Adaptation. IEEE Transactions on Knowledge and Data Engineering (TKDE), 10.1109/TKDE.2021.3119185, 2021.

38. Kui Yu, Mingzhu Cai,  Xingyu Wu, Lin Liu, and Jiuyong Li. Multi-Label Feature Selection: a Local Causal Structure Learning Approach.  IEEE Transactions on Neural Networks and Learning Systems (TNNLS),10.1109/TNNLS.2021.3111288, 2021.

39. Junlong Li**, Peipei Li, Xuegang Hu,  and Kui Yu. Learning common and label-specific features for multi-Label classification with correlation information.  Pattern Recognition, accepted/in press, 2021.

40. Fei Liu*, Xuegang Hu, Chenyang Bu, and Kui Yu. Fuzzy Bayesian Knowledge Tracing. IEEE Transactions on Fuzzy Systems (TFS), 10.1109/TFUZZ.2021.3083177, 2021.

41. Shuai Yang*, Hao Wang, Kui Yu, Fuyan Cao, and Xindong Wu. Towards Efficient Local Causal Structure Learning.  IEEE Transactions on Big Data (TBD).  10.1109/TBDATA.2021.3062937, 2021.

42. Xiulin Zheng**, Peipei Li, Xuegang Hu, Kui Yu. Semi-supervised Classification on Data Streams with Recurring Concept Drift and Concept Evolution. Knowledge-Based Systems, 10.1016/j.knosys.2021.106749, 2021.

43. Shuai Yang*, Kui Yu, Fuyan Cao, Hao Wang, and Xindong Wu. Dual-Representation based Autoencoder for  Domain Adaptation.  IEEE Transactions on Cybernetics (TCYB). 10.1109/TCYB.2020.304076, 2021.

44. Xingyu Wu, Bingbing Jiang, Kui Yu, Huanhuan Chen. Separation and Recovery Markov Boundary Discovery and Its Application in EEG-based Emotion Recognition. Information Science,  571: 262-278, 2021.

45. Yuling Li,* Kui Yu, and Yuhong Zhang. Learning Cross-Lingual Mappings in Imperfectly Isomorphic Embedding Spaces. IEEE Transactions on Audio, Speech and Language Processing (TASLP), 29: 2630-2642 (2021), 2021.

46. Yuling Li*,  Yuhong Zhang,  Kui Yu, and  Xuegang Hu. Adversarial training with Wasserstein distance for learning cross-lingual word embeddings.  Applied Intelligence, https://doi.org/10.1007/s10489-020-02136-x , 2021.

47. Kui Yu, Lin Liu, and Jiuyong Li. A Unified View of Causal and Non-causal Feature Selection.  ACM Transactions on Knowledge Discovery from Data (TKDD), 15(4): 63:1-63:46, 2021.

48. Jiuyong Li, Weijia Zhang, Lin Liu, Kui Yu, Thuc Duy Le, and Jixue Liu. A General Framework for Causal Classification. International Journal of Data Science and Analytics,  11(2): 127-139, 2021.

49. Zhaolong Lin*, Kui Yu, Hao Wang, Lei Li, and Xindong Wu. Using Feature  Selection for Local Causal Structure Learning. IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 5(4): 530-540 (2021).

50. Xinyu Wu, Bingbing Jiang, Kui Yu, Chunyan Miao, and Huanhuan Chen. Accurate Markov Boundary Discovery for Causal Feature Selection.  IEEE Transactions on Cybernetics (TCYB), 50(12): 4983-4996 (2020).

51. Kui Yu, Xianjie Guo, Lin Liu, Jiuyong Li, Hao Wang, Zhaolong Ling, Xindong Wu. Causality-based Feature Selection: Methods and Evaluations.  ACM Computing Surveys, 53(5): 111:1-111:36 (2020).

52. Kui Yu, Lin Liu, Jiuyong Li, Wei Ding, and Thuc Le. Multi-Source Causal Feature Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 42(9): 2240-2256, 2020.

53. Hao Wang, Zhaolong Lin, Kui Yu and Xindong Wu. Towards Efficient and Effective Discovery of Markov Blankets for Feature Selection. Information Science, 509: 227-242 (2020).

54. Kui Yu, Lin Liu, and Jiuyong Li. Learning Markov Blankets from Multiple Interventional Datasets. IEEE Transactions on Neural Networks and Learning Systems (TNNLS),31(6), 2005-2019, 2020. 

55. Chaofan Liu**, Shuai Yang*, Kui Yu. Markov Boundary Learning with Streaming Data for Supervised Classification.  IEEE Access, 8: 102222-102234 (2020).

56. Zhaolong Lin*, Kui Yu, Hao Wang, Lin Liu, Wei Ding, and Xindong Wu. BAMB: A Balanced Markov Blanket Discovery Approach to Feature Selection. ACM Transactions on Intelligent Systems and Technology (TIST),10(5): 52:1-52:25 (2019). 

57. Kui Yu and Huanhuan Chen. Markov boundary-based outlier Mining. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 30(4): 1259-1264 (2019) 

58. Kui Yu, Lin Liu, Jiuyong Li, and Huanhuan Chen. Mining Markov Blanket without Causal Sufficiency. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 29(12): 6333-6347 (2018). 

59. Taosheng Xu, Ning Su, Lin Liu, Junpeng Zhang, Hongqiang Wang, Jie Gui, Kui Yu, Jiuyong Li and Thuc Duy Le. MiRBaseConverter: An R/Bioconductor Package for Converting and Retrieving miRNA Name, Accession, Sequence and Family Information in Different Versions of miRBase.  BMC Bioinformatics: 179-188 (2018).

60. Jing Yang, Xiaoxue Guo, Ning An, Aiguo Wang, and Kui Yu. Streaming Feature-based Causal Structure Learning Algorithm with Symmetrical Uncertainty. Information Sciences, 467: 708-724 (2018). 

61. Kui Yu, Xindong Wu, Wei Ding, Yang Mu, and Hao Wang. Markov Blanket Feature Selection using Representative Sets. IEEE Transactions on Neural Networks and Learning Systems (TNNLS). 28(11): 2775-2788, 2017. 

62. Kui Yu, Wei Ding, and Xindong Wu. LOFS: A Library of Online Streaming Feature Selection. Knowledge-Based Systems (KBS), 113(2016),1-3. 

63. Kui Yu, Xindong Wu, Wei Ding, and Jian Pei. Scalable and Accurate Online Feature Selection for Big Data. ACM Transactions on Knowledge Discovery from Data (TKDD), 11(2),1:39, 2016. 

64. Kui Yu, Xindong Wu, Wei Ding, and Hao Wang. (2012) Exploring Causal Relationships with Streaming Features. The Computer Journal, 55(9): 1103-1117.  

65. Kui Yu, Wei Ding, Dan A. Simovici, Hao Wang, Jian Pei, and Xindong Wu. Classification with Streaming Features: An Emerging Pattern Mining Approach. ACM Transactions on Knowledge Discovery from Data (TKDD), 9(4): 30:1-30:31 (2015).  

66.  Kui Yu, Wei Ding, Hao Wang, and Xindong Wu. (2013) Bridging Causal Relevance and Pattern Discriminability: Mining Emerging Patterns from High-Dimensional Data. IEEE Transactions on Knowledge and Data Engineering (TKDE), 25(12): 2721-2739.

67. Xindong Wu, Kui Yu, Wei Ding, Hao Wang, and Xingquan Zhu. (2013) Online Feature Selection with Streaming Features. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 35(5): 1178-1192. 

Conference Papers (*PhD student, **Master student):

1. Libing Yuan**, Shuaibo Hu**, Kui Yu, Le Wu. Boosting Explainability through Selective Rationalization in Pre-trained Language Models, The 31st SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'25), Toronto, ON, Canada, August 3, 2025-August 7, 2025.

2. Xianjie Guo*, Kui Yu, Lizhen Cui, Han Yu, Xiaoxiao Li. Federated Causally Invariant Feature Learning, The 39th Annual AAAI Conference on Artificial Intelligence (AAAI'25), Philadelphia, Pennsylvania, USA, February 25- March 4, 2025.

3. Zhaolong Ling, Jiale Yu, Yiwen Zhang, Debo Cheng, Peng Zhou, Xingyu Wu, Bingbing Jiang, Kui Yu. Local Causal Discovery without Causal Sufficiency, The 39th Annual AAAI Conference on Artificial Intelligence (AAAI'25), Philadelphia, Pennsylvania, USA, February 25- March 4, 2025.

4. Xianjie Guo*，Kui Yu, Hao Wang, Lizhen Cui, Han Yu, and Xiaoxiao Li. Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection, The 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), Jeju Island, South Korea, August 3-9, 2024.

5. Xianjie Guo*, Kui Yu, Lin Liu, and Jiuyong Li. FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning, The Thirty-eighth AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, British Columbia, Canada,  February 20-27, 2024.

6. Shuaibo Hu**, Kui Yu. Learning Robust Rationales for Model Explainability: A Guidance-based Approach, The Thirty-eighth AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, British Columbia, Canada,  February 20-27, 2024.

7. Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu, Lin Liu, and  Kui Yu. Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder, The Twelfth International Conference on Learning Representations (ICLR'24), Vienna, Austria, May 7-11, 2024. 

8. Jindi Li**, Kui Yu, Yuling Li, and Yuhong Zhang. TransD-Based Multi-Hop Meta Learning for Few-Shot Knowledge Graph Completion. Proceedings of 2023 the International Joint Conference on Neural Networks (IJCNN'23), Gold Coast, Australia,June 18-23, 2023. 

9. Yuwei Wang**, Yuling Li*, Kui Yu, and Yimin Hu. Knowledge-Enhanced Hierarchical Transformers for Emotion-Cause Pair Extraction. Proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'23, 143/822, long paper), Osaka, Japan, May 25-28, 2023.

10. Yuling Li*, Kui Yu, Xiaoling Huang, and Yuhong Zhang. Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing(EMNLP'22), Abu Dhabi, UAE, December 7-11, 2022.

11. Xianjie Guo*, Yujie Wang, Xiaoling Huang, Shuai Yang and Kui Yu. Bootstrap-based Causal Structure Learning. Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM'22), October 17-22, 2022, Atlanta, Georgia, USA.

12. Yiwen Cao**, Kui Yu, Xiaoling Huang,  and Yujie Wang. A New Skeleton-Neural DAG Learning Approach. Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'22), May 16-19, 2022, Chengdu, China.

13. Yunxia Wang*, Fuyuan Cao, Kui Yu, and Jiye Liang. Efficient Causal Structure Learning from Multiple Interventional Datasets with Unknown Targets.  The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI'22), 2022.

14. Yujie Wang**, Shuai Yang, Xianjie Guo, and Kui Yu. Improving Gradient-based DAG Learning by Structural Asymmetry. The 12th IEEE International Conference on Big Knowledge (ICBK 2021), 2021.

15. Wentao Hu**, Shuai Yang, Xianjie Guo, and , Kui Yu. Accelerating Learning Bayesian Network Structures by Reducing Redundant CI Tests. The 12th IEEE International Conference on Big Knowledge (ICBK 2021), 2021.

16. Xiang Wang,  Xiaoyong Li, Junxing Zhu, Zichen Xu, Kaijun Ren, Weimin Zhang, Xinwang Liu, and Kui Yu. A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks. International Conference on Database Systems for Advanced Applications (DASFAA 2021), 376-391, 2021.

17. Debo Cheng*, Jiuyong Li, Lin Liu, Jixue Liu, Kui Yu and Thuc Duy Le. Causal query in observational data with hidden variables.  The 24th European Conference on Artificial Intelligence (ECAI'20), Santiago de Compostela, Spain, June 8-12, 2020. 

18. Xinyu Wu, Bingbing Jiang, Kui Yu, Huanhuan Chen, and Chunyan Miao. Multi-label Causal Feature Selection.  The 34th AAAI Conference on Artificial Intelligence (AAAI'20), February 7-12, 6430-6437, New York, USA.

19. Bingbing Jiang, Xinyu Wu**, Kui Yu, and Huanhuan Chen. Joint Semi-supervised Feature Selection and Classification through Bayesian Approach. The 33th AAAI Conference on Artificial Intelligence (AAAI'19), 3983-3990, January 27- February 1, 2019 , Honolulu, Hawaii, USA. 

20.  Yong Zhuang, Kui Yu, Dawei Wang, and Wei Ding. An Evaluation of Big Data Analytics in Feature Selection for Long-lead Extreme Floods Forecasting. Proceedings of the 13th IEEE International Conference on Networking, Sensing and Control (ICNSC 2016), Mexico City, Mexico, April 28-30, 2016.

21. Kui Yu, Dawei Wang, Wei Ding, David L. Small, Shafiqul Islam, Jian Pei, and Xindong Wu. Tornado Forecasting with Multiple Markov Boundaries. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD 2015), 10-13 August, Sydney, Australia.

22. Kui Yu, Xindong Wu, Wei Ding, and Pei Jian. Towards Scalable and Accurate Online Feature Selection for Big Data. Proceedings of the 14th IEEE International Conference on Data Mining (ICDM 2014), Shenzhen,China, December 14-17, 2014. 

23. Kui Yu, Xindong Wu, Zan Zhang, Yang Mu, Hao Wang, and Wei Ding. Markov Blanket Feature Selection with Non-Faithful Data Distributions. Proceedings of the 13th IEEE International Conference on Data Mining (ICDM 2013), Dallas, Texas, December 7-10, 2013，857-866.

24. Dawei Wang, Wei Ding, Kui Yu, Xindong Wu, Ping Chen, David L. Small, and Shafiqul Islam. Towards long-lead forecasting of extreme flood events: a data mining framework for precipitation cluster precursors identification. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2013), Chicago, IL, USA, August 11-14, 2013, 1285-1293.

25. Kui Yu, Wei Ding, Dan A. Simovici, and Xindong Wu. Mining Emerging Patterns by Streaming Feature Selection. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), Beijing, China, August 12-16, 2012, 60-68.

26. Kui Yu, Xindong Wu, Wei Ding, and Hao Wang. Causal Associative Classification. Proceedings of the 11th IEEE International Conference on Data Mining (ICDM 2011), Vancouver, Canada, December 11-14, 2011, 914-923.

27.  Xindong Wu, Kui Yu, Hao Wang, and Wei Ding. Online Streaming Feature Selection. Proceedings of the 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, June 21-24, 2010, 1159-1166.

28. Kui Yu, Xindong Wu, Hao Wang, and Wei Ding. Causal Discovery from Streaming Features. Proceedings of the 10th IEEE International Conference on Data Mining (ICDM 2010), Sydney Australia, December 14-17, 2010, 1163-1168.

29. Kui Yu, Hao Wang, and Xindong Wu. A Parallel Algorithm for Learning Bayesian Networks. Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007) , Nanjing, China, May 22-25, 2007, 1055-1063.

Journal Papers in Chinese

1. 杨帅*，王浩，俞奎，曹付元. 基于实例加权和双分类器的稳定学习算法.  软件学报，2021

2. 姚宏亮，贾虹宇，杨静，俞奎. 基于分层动态贝叶斯网络的股市趋势扰动推理算法. 模式识别与人工智能，2022.

Software

1. CausalFS: An open-source package of causal feature selection and causal (Bayesian network) structure learning (C/C++ version)  

https://github.com/kuiy/CausalFS

2.  pyCausalFS: An open-source package of causal feature selection and causal (Bayesian network) structure learning (Python version)  

https://github.com/wt-hu/pyCausalFS

https://github.com/kuiy/pyCausalFS

3. Causal Learner: An open-source package of causal feature selection and causal (Bayesian network) structure learning (Matlab version)

http://bigdata.ahu.edu.cn/causal-learner 

4. LOFS: A Library of Online Streaming Feature Selection.

https://github.com/kuiy/LOFS

5. Source codes for the paper “Towards Efficient Local Causal Structure Learning”

https://github.com/kuiy/ELCS 

6.  Source codes for the paper “Dual-Representation based Autoencoder for Domain Adaptation”

https://github.com/kuiy/DRAE 

6. Source codes for the paper “Towards Privacy-Aware Causal Structure Learning in Federated Setting"

https://github.com/Xianjie-Guo 

7.  Source codes for the paper “Adaptive Skeleton Construction for Accurate DAG Learning "

   https://github.com/Xianjie-Guo 

8.  Source codes for the paper "Causal Feature Selection with Dual Correction" and "Error-Aware Markov Blanket Learning for Causal Feature Selection" 

   https://github.com/Xianjie-Guo 

9. Source codes for the paper "Learning Causal Representations for Robust Domain Adaptation"

https://github.com/kuiy/Causal-Autoencoder-CAE- 

10. Other source codes please see:

https://github.com/kuiy

https://github.com/Xianjie-Guo 

Honors and Awards

2014: The Appreciation award of Academy of Science and Engineering (ASE) for contributions as program track chair, BigDataScience 2014, Beijing.

2014: The PIMS (Pacific Institute for the Mathematical Sciences) Post-Doctoral Fellowship Award for the 2014- 2015 academic year, Canada ( (14 awardees in 2014 in Canada)

2014: CCF (China Computer Federation) Outstanding Doctoral Dissertation Award (10 awardees per year in China)

2014: Best reviewer Award (SCIENCE CHINA Information Sciences)

2013: The Ebco Eppich Fellowship award of the School of Computing Science, Simon Fraser University, Canada

2012: National Scholarship for Graduate students of China

2010: ICDM’10 student travel award

2007: PAKDD’07 student travel award

Project

1. "Research on Privacy-Preserving  Causal Inference  in Federeated Setting", National Natural Science Foundation of China (NSFC), 62376087, 2024.1-2027.12, PI

3. "Research on Fundamental Theories and Methodologies of Cognitive computation",  National Key Research and Development Program of China (MOST), 2020AAA0106100, 2020.11-2024.10,  PI

4. "Cross-media Causal Inference and Decision Analysis",  National Key Research and Development Program of China (MOST), 2021ZD0111801, 2021.12-2025.11,  Co-PI

5. "Local causal discovery from multiple source datasets with high dimensionality", National Natural Science Foundation of China (NSFC), 61876206, 2019.1-2022.12, PI

6. " Causal inference in high-dimensional data", Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province, CICIP2020003, 2020.10.-2022.09, PI

Professional Activities

  Conference Services:

• PC member: ICML'22, IJCAI'22-24, KDD'22-24, SDM'22, PAKDD'22, AAAI'22-24, KDD'21, ICDM'21, CIKM'21, PAKDD'21, AAAI'21, AAAI'20, KDD'20, CIKM'20, ICDM'20, ECAI'20 (SPC), PAKDD'20, KDD'19, CIKM'19, AAAI'19, PAKDD'19, KDD'18, PAKDD'18,CIKM'18, ICBK'18, IJCAI'17,CIKM'17, SIGKDD'16-17, IEEE ICDM'16, ICNC'15-16, IJCAI'15, IEEE ICTAI’14-15, BigComp’15, IMMM’15, IEEE ICDM15 PhD. Forum, IEEE ICDM’14.

• PC member: The 2016-2019 ACM SIGKDD Workshop on Causal Discovery (in conjunction with KDD'16, KDD’17, KDD'18, KDD'19)

• Publicity chair: the 2016 IEEE International Conference on Data Science in Cyberspace, Changsha, China, June 13-16, 2016; the 5th International Workshop on Climate Informatics, Boulder, USA, September 24-25, 2015

• Program vice co-chair: The third IEEE/ASE International Conference on Big Data Science and Computing, Beijing, China, August 4-7, 2014.

Journal Reviewer

• ACM Transactions on Knowledge Discovery from Data

• IEEE Transactions on Neural Networks and Learning Systems.

• IEEE Transactions on Knowledge and Data Engineering

• IEEE Transactions on Emerging Topics in Computational Intelligence

• ACM Transactions on Intelligent Systems and Technology

• Machine Learning

• Knowledge and Information Systems (Springer)

• Knowledge-based Systems (Elsevier)

• SCIENCE CHINA Information Sciences (Springer)

Talks

• "Causality-based Feature Selection and beyond", Beijing Normal University, Beijing, November, 30, 2019.

• "Causal Feature Selection and its Applications", Shanxi University, April, 1, 2019, Taiyuan, Shanxi.

• "Local causal discovery and feature selection", Tsinghua University，November, 24, 2018, Beijing, China.

• “Towards scalable and reliable causal data mining methods” , University of Science and Technology of China, May 20, 2017, Hefei, China.

• “Online streaming feature learning for big data”, the 3th IEEE International Conference on Big Knowledge, 2012, August 9-10, Hefei, China.