个人简介
俞奎，合肥工业大学计算机与信息学院教授，博士生导师。2013年6月毕业于合肥工业大学计算机与信息学院，获工学博士学位(导师吴信东教授和王浩教授); 2011.08-2012.08作为国家公派联合培养博士生在美国University of Massachusetts Boston计算机系从事合作研究(导师Wei Ding 教授); 2013.10-2015.09在加拿大Simon Fraser University计算科学学院从事博士后研究(合作导师Jian Pei教授); 2015.10-2018.01作为研究员在澳大利亚University of South Australia信息技术与数学科学学院从事全职研究工作(合作导师jiuyong Li教授和Lin Liu教授)。 曾获2014中国计算机学会优秀博士学位论文奖和2014年度加拿大PIMS博士后奖。主持科技部新一代人工智能国家科技重大专项（科技创新2030新一代人工智能重大项目）课题、国家自然科学基金面上项目多项。安徽省人工智能学会副理事长，安徽省人工智能学会因果与认知智能专委会主任，中国人工智能学会粒计算与知识发现专委会常务委员，因果与不确定性人工智能专委会委员；担任多个国际人工智能领域顶级会议的领域主席和程序委员会委员（如ICML、AAAI、KDD、IJCAI、CIKM等）和国际重要期刊的审稿人。
研究方向
- 因果推断理论与方法
- 强泛化与高可解释模型与方法
- 知识图谱与可解释自然语言处理
- 图像处理与视觉问答
课题组
获奖情况
- 2022年度安徽省计算机学会优秀硕士学位论文奖 (导师)
- 2022年度安徽省优秀硕士学位论文奖 (导师)
- 2021年第三届全国高校计算机能力挑战赛(决赛)优秀指导老师（本科）
- 2021年度安徽省计算机学会优秀硕士学位论文奖 (导师)
- 2021年度第七届安徽省互联网+(决赛）优秀指导老师（本科）
- 2014年度中国计算机学会优秀博士学位论文奖 (全国共10名)
- 2014年度加拿大亚太数学科学研究院（PIMS）博士后奖学金（全国共14名）
- 2014年度ASE (Academy of Science and Engineering)大数据科学奉献奖
- 2013年度西蒙菲沙大学博士后奖学金 (Ebco Eppich Fellowship Award)。
主要论著
专著:
-
俞奎, 王浩, 梁吉业. 因果推断导论. 机械工业出版社, ISBN：9787111731078, 2023年8月.
Preprint (*博士生, **硕士生):
- 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).
- Kui Yu, Jiuyong Li, and Lin Liu. A Review on Algorithms for Constraint-based Causal Discovery. arXiv:1611.03977 [cs.AI] (2016).
Journal Paper (*博士生, **硕士生):
- Kui Yu , Chen Rong, Hao Wang, Fuyuan Cao, and Jiye Liang. Federated local causal structure learning, SCIENCE CHINA Information Sciences(SCIS), accepted, 2024.
- 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.
- 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.
- Yujie Wang*, Kui Yu, Guodu Xiang, Fuyuan Cao, Jiye Liang. Discovering Causally Invariant Features for Out-of-Distribution Generalization. Pattern Recognition (PR), accepted, 2024.
- 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.
- Zhaolong Ling, Bo Li, Yiwen Zhang, Peng Zhou, Xingyu Wu, Yuee Huang,Kui Yu, Xindong Wu. Causal Discovery Using Weight-Based Conditional Independence Test. ACM Transactions on Knowledge Discovery from Data (TKDD), accepted, 2024.
- 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.
- 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.
- 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.
- Yuhong Zhang, Yunlong Ji, Kui Yu, Xuegang Hu, Xindong Wu. A cross-network node classification method in open-set scenario. Pattern Recognition, 155: 110718, 2024.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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), DOI: 10.1109/TKDE.2022.3218786,2022.
- 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), 163:92-95,2022.
- Zhaolong Ling, Bo Li, Yiwen Zhang, Kui Yu, and Xindong Wu. Causal Feature Selection with Efficient Spouses Discovery. IEEE Transactions on Big Data (TBD), DOI: 10.1109/TBDATA.2022.3178472,2022.
- 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), DOI: 10.1109/TNNLS.2022.3178075, 2022.
- 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), DOI: 10.1109/TBDATA.2022.3164239, 2022.
- 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), 36(3): 1174-1196,2022.
- Xianjie Guo*, Kui Yu, Fuyuan Cao, Peipei Li, and Hao Wang. Error-Aware Markov Blanket Learning for Causal Feature Selection. Information Science, 589: 849-877, 2022.
- 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), 16(5): 1-25, 2022.
- 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), DOI: 10.1109/TNNLS.2021.3139389, 2022.
- Debo Cheng*, Jiuyong Li, Lin Liu, Kui Yu, Thuc Duy Le, Jixue Liu. Toward Unique and Unbiased Causal Effect Estimation From Data With Hidden Variables. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), DOI: 10.1109/TNNLS.2021.3133337,2022.
- Kui Yu, Yajing Yang, and Wei Ding. Causal Feature Selection with Missing Data. ACM Transactions on Knowledge Discovery from Data (TKDD), 16(4): 1-24, 2022.
- 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.
- 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.
- Junlong Li**, Peipei Li, Xuegang Hu, and Kui Yu. Learning common and label-specific features for multi-Label classification with correlation information. Pattern Recognition, 121:108259, 2022.
- Fei Liu*, Xuegang Hu, Chenyang Bu, and Kui Yu. Fuzzy Bayesian Knowledge Tracing. IEEE Transactions on Fuzzy Systems (TFS), 30(7):2412 - 2425, 2022.
- Shuai Yang*, Hao Wang, Kui Yu, Fuyan Cao, and Xindong Wu. Towards Efficient Local Causal Structure Learning. IEEE Transactions on Big Data (TBD). 8：1592-1609, 2022.
- Shuai Yang*, Kui Yu, Fuyan Cao, Hao Wang, and Xindong Wu. Dual-Representation based Autoencoder for Domain Adaptation. IEEE Transactions on Cybernetics (TCYB). 52(8), 7464 - 7477, 2022.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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).
- 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).
- 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.
- 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).
- 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.
- 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).
- Kui Yu and Huanhuan Chen. Markov boundary-based outlier Mining. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 30(4): 1259-1264 (2019)
- 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).
- 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.
- Kui Yu, Wei Ding, and Xindong Wu. LOFS: A Library of Online Streaming Feature Selection. Knowledge-Based Systems (KBS), 113(2016),1-3.
- 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.
- 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).
- 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.
- 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 Paper (*博士生, **硕士生)：
- 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.
- Xianjie Guo* and 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.
- Shuaibo Hu** and 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.
- 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.
- 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.
- 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.
- 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.
- 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,long paper), Abu Dhabi, UAE, December 7-11, 2022.
- 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, regular paper), October 17-22, 656–665, 2022, Atlanta, Georgia, USA.
- 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, 626–638, 2022, Chengdu, China.
- 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), 36(8), 8584-8593, 2022.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
中文期刊文章 (*博士生, **硕士生):
- 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
- 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.
发明专利:
- 俞奎，刘超凡，李培培. 一种面向动态医疗数据的因果特征提取方法, 申请号：202111196507.5, 专利申请日期：2021年10月14日.
- 俞奎，王雨薇，李玉玲，解弘艺. 一种基于元学习与知识图谱的商品推荐方法, 申请号：202210397786.X, 专利申请日期：2022年4月15日.
- 俞奎，李金迪，李玉玲，王雨薇. 一种基于图注意力机制的因果常识知识库构建方法, 申请号：2023108588665, 专利申请日期：2023年7月13日.
- 俞奎，王雨薇， 李玉玲，李金迪. 一种基于元学习的医学常识知识图谱自动化构建方法, 申请号：2023108588650, 专利申请日期：2023年7月13日.
- 俞奎，相国督，董露露，蒋曼青. 从医学知识图谱中抽取因果关系的方法、介质及设备，申请号：202411310647.4, 专利申请日期：2024年10月15日.
- 俞奎，缪佳李. 基于因果引导视觉注意力表征视觉问答方法、介质及设备，申请号：202411310645.5, 专利申请日期：2024年10月15日.
- 俞奎，王雨薇，董露露，杨静. 面向商品评论的情感因果对抽取方法、介质及设备，申请号：202411324378.7, 专利申请日期：2024年10月15日.
学术兼职
- 安徽省人工智能学会副理事长
- 安徽省人工智能学会因果与认知智能专委会主任
- 中国人工智能学会粒计算与知识发现专委会常务委员
- 中国人工智能学会不确定性人工智能专委会委员
- 中国计算机学会人工智能与模式识别专业委员会通讯委员
- 国际顶级学术会议程序委员会领域委员(Senior PC/PC member)
- IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) Associate Editor
国际顶级学术会议程序委员会委员(Senior PC/PC member):
- ICML: ICML'22
- AAAI: AAAI'23-24, AAAI'22, AAI'21, AAAI'20
- IJCAI: IJCAI'22-24, IJCAI'17, IJCAI'15
- KDD: KDD'22-24, KDD'21, KDD'20, KDD'19, KDD'18, KDD'16, KDD'17
- CIKM: CIKM'22-24,CIKM'21, CIKM'20, CIKM'19, CIKM'18, CIKM'17
- PAKDD: PAKDD'22-23, PAKDD'21, PAKDD'20, PAKDD'19, PAKDD'18, PAKDD'17, PAKDD'16
- The 2016-2024 ACM SIGKDD Workshop on Causal Discovery (in conjunction with KDD'16, KDD'17, KDD'18, KDD'19)
期刊审稿:
- 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)
会议/论坛组织:
- 论坛主席：2024年安徽省认知智能与因果分析论坛第一期（2024年5月16-17日，合肥）
- 论坛主席： 中国计算机学会2023年计算机应用大会（NCCA'23） “因果机器学习及其应用论坛” （2023年7月16-19日，苏州）
- 大会主席：The 13th IEEE International Conference on Knowledge Graph (ICKG'22) （2022年11月30日-12月1日线上）
- 论坛主席： 安徽省人工智能学会2022年 “可信机器学习论坛” （2022年12月3日线上）
- 论坛主席： 中国计算机学会2021年人工智能大会 (CCFAI'21) “因果学习论坛” （2021年7月22-25日，烟台）
