个人简历

Ph.D. Associate Professor, LAMDA Group
National Key Laboratory for Novel Software Technology, Nanjing University, China
Brief CV
I am an associate professor of National Key Laboratory for Novel Software Technology in Nanjing University and a faculty member of LAMDA Group, led by professor Zhi-Hua Zhou.[Curriculum Vitae]
Research Interests
My current research interests mainly include Machine Learning and Data Mining. More specifically, I am interested in:
Semi-supervised learning and weakly supervised learning
Statistical learning and optimization
Applications on image, text, graph, video data and others
Publications(* indicates my student) [LAMDA Publications][Google Scholar Citations]
Services
Conference Committee:
Program Co-Chair, The 18th China Conference on Machine Learning (CCML2021), Aug. 2021, Changsha, China
Program Co-Chair, The 18th China Symposium on Machine Learning and Applications (MLA2020), Nov. 2020, Nanjing, China
Program Co-Chair, The IEEE International Conference on Big Data and Smart Computing (BigCOMP2020), Feb. 2020, Busan, Korea
Journal Track Co-Chair, the 13th Asian Conference on Machine Learning (ACML21), Nov. 2021, Virtual
Workshop Co-Chair, the 10th Asian Conference on Machine Learning (ACML18), Nov. 2018, Beijing, China
Tutorial Co-Chair, the IEEE International Conference on Big Data and Smart Computing (BigCOMP2021), Feb. 2021, Bangkok, Thailand
Tutorial Co-Chair, the 11th Asian Conference on Machine Learning (ACML19), Nov. 2019, Nagoya, Japan
Area Chair, ICML22/ICML21
Area Chair, IJCAI21
Member, Senior Program Committee, AAAI22/20/19
Member, Senior Program Committee, IJCAI22/20/19/17/15
Member, Senior Program Committee, ACML20/19/18/17
Member, Senior Program Committee, PAKDD22/20/19
Member, Senior Program Committee, ECAI20
Member, Program Committee, ICML20/19/18/17/16/15/14
Member, Program Committee, NIPS21/20/19/18/17/16/15/14
Member, Program Committee, KDD21/20/19/18/17/16/15
Member, Program Committee, AAAI21/18/17/16
Member, Program Committee, IJCAI18/16
Member, Program Committee, CVPR21/19/18/17/16
Member, Program Committee, ICCV21/17/15
Member, Program Committee, ACML16/15/14
Member, Program Committee, ICLR21/19
Member, Program Committee, AISTATS21/20/19/18/17
Member, Program Committee, PAKDD18
Member, Program Committee, PRICAI19/18
Journal:
Editorial Board Member, Machine Learning (2021-)
Action Editor, Nerual Network (2020-)
Young Editor, Frontiers of Computer Science (2020-)
Guest Editor, Science China: Information Science 2020 Special Focus on Weakly Supervised Learning;
Guest Editor, Journal of Computer Science and Technology (JCST) 2019 Special Section on Learning and Mining in Dynamic Enviroments;
Editorial Board Member, Machine Learning Journal (Special Issue on ACML17 Journal Track; Special Issue on ACML18 Journal Track);
Reviewer for Artificial Intelligence (AIJ)
Reviewer for Journal of Artificial Intelligence Research (JAIR)
Reviewer for Journal of Machine Learning Research (JMLR)
Reviewer for IEEE Transcations on Pattern Analysis and Machine Intelligence (TPAMI)
Reviewer for Machine Learning (MLJ)
Reviewer for IEEE Transcations on Knowledge and Data Engineering (TKDE)
Reviewer for IEEE Transcations on Neural Network and Learning Systems (TNNLS)
Reviewer for IEEE Transcations on Big Data (TBD)
Reviewer for Data Mining and Knowledge Discovery (DMKD)
Reviewer for Neural Computation
Reviewer for Pattern Recognition
Reviewer for Science China Information Science
Workshop Organization:
Co-organizer of IJCAI workshop on Long-Tailed Distribution Learning, Jul. 2021, Virtual
Co-organizer of PAKDD workshop on Weakly Supervised Learning, Apr. 2019, Macau, China
Co-organizer of IJCNN Special Session on Machine Learning with Incompletely Labeled Data, July 2016, Vancouver, Canada
Co-organizer of ACML workshop on Machine Learning in China, Nov. 2015, Beijing, China
Co-organizer of The 16th China Symposium on Machine Learning and Applications (MLA2018), Nov. 2018, Nanjing, China
Co-organizer of The 15th China Symposium on Machine Learning and Applications (MLA2017), Nov. 2017, Beijing, China
Co-organizer of The 14th China Symposium on Machine Learning and Applications (MLA2016), Nov. 2016, Nanjing, China
Co-organizer of The 13rd China Symposium on Machine Learning and Applications (MLA2015), Nov. 2015, Nanjing, China
Professional Organization:
Secretary General, CAAI-ML (China Association of Artificial Intelligence-Machine Learning Committee), 2021-
Secretary General, JSAI-ML (Jiangsu Association of Artificial Intelligence-Machine Learning Committee), 2017-
Secretary Member, CCF-AIPR (China Computer Federation-Artificial Intelligence and Pattern Recognition Committee), 2017-
Member, IEEE CIS Neural Networks Committee, 2019-2020
Honers and Awards
Baidu Scholarship (Lan-Zhe Guo), 2021;
Microsoft Scholarship runner-up (Lan-Zhe Guo), 2021;
Supervior for Excellent Master Thesis (runner-up) (Yong-Nan Zhu), Nanjing University, 2021;
Supervior for Excellent Master Thesis (Qian-Wei Wang, De-Ming Liang), Nanjing University, 2020;
Supervior for Excellent Undergraduate Thesis (Hao Liu), Nanjing Uninversity, 2018;
Best Student Paper Award (with Yuan-Zhao Li, Shao-Bo Wang, graduate student), CCDM, 2016;
Best Student Paper Award (with Shao-Bo Wang, graduate student), CCML, 2015;
Outstanding Doctoral Dissertation Award, Jiangsu Province, 2014;
Outstanding Doctoral Dissertation Award, Nanjing University, 2014;
Outstanding Doctoral Dissertation Award, CCF (China Computer Federation), 2013;
Best Student Paper Award, CCDM, 2011;
Research Travel Award, ICML, 2011;
Microsoft Fellowship Award, 2009;
Research Travel Award, ICML, 2009;
Courses and Teaching Assistant
Introduction to Advanced Machine Learning (For graduate students, Spring 2022; Spring 2021)
Introduction to Machine Learning (For undergraduate students, Fall 2021; Fall 2020; Fall, 2019)
Digital Image Processing. (For undergraduate students, Spring, 2019; Spring, 2018, 2017, 2016, 2015, 2014)
Introduction to Data Mining. (For undergraduate students, Teaching Assistant, Spring, 14)
Data Mining (081202B03). (For graduate students, Teaching Assistant, Fall, 08)
Discrete Mathematis. (For undergraduate students, Teaching Assistant, Spring, 07)
Seminar
Optimization Seminar (for LAMDA member only, Fall, 12)

研究领域

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近期论文

Journal Paper
Changjian Chen, Zhaowei Wang, Jing Wu, Xiting Wang, Lan-Zhe Guo, Yu-Feng Li, Shixia Liu. Interactive Graph Construction for Graph-Based Semi-Supervised Learning. IEEE Transactions on Visualization and Computer Graphics (TVCG), 27(9): 3701-3716, 2021
Tong Wei*, Hai Wang, Wei-Wei Tu, Yu-Feng Li. Robust Model Selection for PU Learning under Constraint. Science CHINA Information Science, In press.
Yu-Feng Li, De-Ming Liang. Lightweight Label Propagation for Large-Scale Network Data. IEEE Transactions on Knowledge and Data Engineering (TKDE), 33(5): 2071-2082, 2021.
Yu-Feng Li, Lan-Zhe Guo, Zhi-Hua Zhou. Towards Safe Weakly Supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(1): 334-346, 2021. [code]
Tong Wei*, Yu-Feng Li. Does Tail Label Help for Large-Scale Multi-Label Learning. IEEE Transactions on Neural Network and Learning Systems (TNNLS), 31(7): 2315-2324, 2020.
Miao Xu, Yu-Feng Li, Zhi-Hua Zhou. Robust Multi-Label Learning with PRO Loss. IEEE Transactions on Knowledge and Data Engineering (TKDE). 32(8): 1610-1624, 2020.
Yu-Feng Li, De-Ming Liang. Safe Semi-Supervised Learning: A Brief Introduction. Frontiers of Computer Science (FCS). 2019, 13(4): 669-676.
Tong Wei*, Lan-Zhe Guo, Yu-Feng Li, Wei Gao. Learning Safe Multi-Label Prediction for Weakly Labeled Data. Machine Learning (MLJ). 107(4): 703-725, 2018. [code]
Hai Wang*, Shao-Bo Wang, Yu-Feng Li. Instance Selection Method for Improving Graph-Based Semi-Supervised Learning. Frontiers of Computer Science (FCS). 12(4): 725-735, 2018.
Shao-Bo Wang* and Yu-Feng Li. Classifier Circle Method for Multi-Label Learning. Journal of Software, 2015, 26(11): 2811-2819. (In chinese with english abstract).[code]
Yu-Feng Li and Zhi-Hua Zhou. Towards Making Unlabeled Data Never Hurt. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 37(1):175-188, 2015. [code]
Yu-Feng Li, Ivor Tsang, James Kwok and Zhi-Hua Zhou. Convex and Scalable Weakly Labeled SVMs. Journal of Machine Learning Research (JMLR), 14:2151-2188, 2013. CORR abs/1303.1271. [code]
Rong Jin, Tian-Bao Yang, Mehrdad Mahdavi, Yu-Feng Li and Zhi-Hua Zhou. Improved Bounds for the Nystrom Method with Application to Kernel Classification. IEEE Transactions on Information Theory (IEEE TIT). 59(10): 6939-6949, 2013.
Zhi-Hua Zhou, Min-Ling Zhang, Sheng-Jun Huang and Yu-Feng Li. Multi-Instance Multi-Label Learning. Artificial Intelligence (AIJ), 2012, 176(1): 2291-2320. [code]
Yu-Feng Li, James T. Kwok, and Zhi-Hua Zhou, Combo-Dimensional Kernels for Graph Classification. Chinese Journal of Computers (in chinese with english abstract), 2009, 32(5):946-952.
Book Chapter
Yu-Feng Li and Zhi-Hua Zhou. Research on Semi-Supervised SVMs. Book Chapter of 'Machine Learning and its Applications 2015
Conference Paper
Zhi Zhou*, Lan-Zhe Guo*, Zhanzhan Cheng, Yu-Feng Li, Shiliang Pu. STEP: Out-of-Distribution Detection in the Presence of Limited In Distribution Labeled Data.. In: Advances in Neural Information Processing Systems (NeurIPS'21) (NeurIPS'21), Virtual Conference, 2021.
Zhi-Fan Wu*, Tong Wei*, Jianwen Jiang, Chaojie Mao, Mingqian Tang, Yu-Feng Li. NGC: A Unified Framework for Learning with Open-World Noisy Data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV'21,Oral). 2021.
Yi Xu, Lei Zhang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, Rong Jin. Dash: Semi-Supervised Learning with Dynamic Thresholding. In: Proceedings of the 38th International Conference on Machine Learning (ICML'21). 2021.
Tong Wei*, Jiang-Xin Shi, Yu-Feng Li. Probabilistic Label Tree for Streaming Multi-Label Learning. In:Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21), 2021
Tong Wei*, Wei-Wei Tu, Yu-Feng Li, Guo-Ping Yan. Towards Robust Prediction on Tail Labels. In:Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21), 2021
Lan-Zhe Guo*, Zhi Zhou*, Jie-Jing Shao, Yu-Feng Li and Didi Collaborators. Learning from Imbalanced and Incomplete Supervision with Its Application to Ride-Sharing Liability Judgment. In:Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21), 2021
Jie-Jing Shao*, Zhanzhan Cheng, Yu-Feng Li, Shiliang Pu. Towards Robust Model Reuse in the Presence of Latent Domains. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), 2021.
Le-Wen Cai, Wang-Zhou Dai, Yu-Xuan Huang, Yu-Feng Li, Stephen Muggleton, Yuan Jiang. Abductive Learning with Ground Knowledge Base. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), 2021.
Tao Han*, Wei-Wei Tu, Yu-Feng Li. Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability. In: Proceedings of the 35th AAAI conference on Artificial Intelligence (AAAI'21), 2021.
Yu-Xuan Huang, Wang-Zhou Dai, Jian Yang, Le-Wen Cai, Shaofen Cheng, Ruizhang Huang, Yu-Feng Li, Zhi-Hua Zhou. Semi-Supervised Abductive Learning and Its Application to Theft Judicial Sentencing. In: Proceedings of the 20th International Conference on Data Mining (ICDM'20). 2020.
Yong-Nan Zhu*, Xiaotian Luo, Yu-Feng Li, Bin Bu, Kaibo Zhou, Wenbin Zhang, Mingfan Lu. Heterogeneous Mini-Graph Neural Network and Its Application to Fraud Invitation Detection. In: Proceedings of the 20th International Conference on Data Mining (ICDM'20). 2020.
Lan-Zhe Guo*, Zhen-Yu Zhang, Yuan-Jiang, Yu-Feng Li, Zhi-Hua Zhou. Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data. In: Proceedings of the 37th International Conference on Machine Learning (ICML'20). 2020. [code][Errata]
Lan-Zhe Guo*, Zhi-Zhou, Yu-Feng Li. RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift. In:Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'20), San Diego, CA, 2020. [code]
Lan-Zhe Guo*, Feng Kuang, Zhang-Xun Liu, Yu-Feng Li, Nan Ma, Xiao-Hu Qie. Weakly-Supervised Learning Meets Ride-Sharing User Experience Enhancement. In: Proceedings of the 34rd AAAI conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.
Yong-Nan Zhu*, Yu-Feng Li. Semi-Supervised Streaming Learning with Emerging New Labels. In: Proceedings of the 34th AAAI conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.[code]
Qian-Wei Wang*, Liang Yang, Yu-Feng Li. Learning from Weak-Label Data: A Deep Forest Expedition. In: Proceedings of the 34th AAAI conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.
Feng Shi*, Yu-Feng Li. Rapid Performance Improvement through Active Model Reuse. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macau, China. 2019, pp.3404-3410. [code]
Tong Wei*, Wei-Wei Tu, Yu-Feng Li. Learning for Tail Label Data: A Label-Specific Feature Approach. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macau, China. 2019, pp.3842-3848.
Qian-Wei Wang*, Yu-Feng Li, Zhi-Hua Zhou. Partial Label Learning with Unlabeled Data. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macau, China. 2019, pp.3755-3761. [code]
Yu-Feng Li, Hai Wang, Tong Wei, Wei-Wei Tu. Towards Automated Semi-Supervised Learning. In: Proceedings of the 33rd AAAI conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019, pp.4237-4244. [code]
Tong Wei*, Yu-Feng Li. Learning Compact Model for Large-Scale Multi-Label Learning. In: Proceedings of the 33rd AAAI conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019, pp.5385-5392. [code]
Lan-Zhe Guo*, Tao Han, Yu-Feng Li. Robust Semi-Supervised Representation Learning for Graph-Structured Data. In: Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'19). Macau, China. 2019, pp.131-143.
Tong Wei*, Yu-Feng Li. Does Tail Label Help for Large-Scale Multi-Label Learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.2847-2853. [code]
De-Ming Liang*, Yu-Feng Li. Lightweight Label Propagation for Large-Scale Network Data. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.3421-3427. [code]
De-Ming Liang*, Yu-Feng Li. Learning Safe Graph Construction from Multiple Graphs. In: Proceedings of the 1st CCF International Conference on Artificial Intelligence (CCF-ICAI18), Spring, 2018, 41-54.
Lan-Zhe Guo*, Yu-Feng Li. A General Formulation for Safely Exploiting Weakly Supervised Data. In: Proceedings of the 32nd AAAI conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018, pp.3126-3133. [code]
Hao-Chen Dong*, Yu-Feng Li, Zhi-Hua Zhou. Learning from Semi-Supervised Weak Label Data. In: Proceedings of the 32nd AAAI conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018, pp.2926-2933. [code]
Yu-Feng Li, Han-Wen Zha, Zhi-Hua Zhou. Learning Safe Prediction for Semi-Supervised Regression. In: Proceedings of the 31st AAAI conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017, pp.2217-2223. [code][Supplemental Material]
Hai Wang*, Shao-Bo Wang, Yu-Feng Li. Instance Selection Method for Improving Graph-Based Semi-Supervised Learning. In: Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI'16), Phuket, Thailand, 2016, pp.565-573.
Yu-Feng Li, Shao-Bo Wang, Zhi-Hua Zhou. Graph Quality Judgement: A Large Margin Expedition. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), New York, NY, 2016, pp.1725-1731. [code]
Xinyue Liu, C. Aggarwal, Yu-Feng Li, Xiangnan Kong, Xinyuan Sun and S. Sathe. Kernelized Matrix Factorization for Collaborative Filtering. SIAM International Conference on Data Mining (SDM'16), Miami, FL. 2016, pp. 378-386.
Yu-Feng Li, James Kwok and Zhi-Hua Zhou. Towards Safe Semi-Supervised Learning for Multivariate Performance Measures. In: Proceedings of the 30th AAAI conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016, pp. 1816-1822.
Wei Gao, Lu Wang, Yu-Feng Li and Zhi-Hua Zhou. Risk Minimization in the Presence of Label Noise. In: Proceedings of the 30th AAAI conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016, pp.1575-1581.
Miao Xu, Yu-Feng Li, and Zhi-Hua Zhou. Multi-Label Learning with Proloss. In: Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI'13), Bellevue, WA, 2013, pp.998-1004.
Tian-Bao Yang, Yu-Feng Li, Mehrdad Mahdavi, Rong Jin, and Zhi-Hua Zhou. Nystrom Method vs Random Fourier Features: A Theoretical and Empirical Comparison. In Bartlett, P., Pereira, F.C.N., Burges, C.J.C., Bottou, L. & Weinberger, K.Q. editors. Advanced in the Neural Information Processing Systems (NIPS'12), Lake Tahoe, NV, 2012, pp.485-493.
Yu-Feng Li, Ju-Hua Hu, Yuang Jiang and Zhi-Hua Zhou. Towards Discovering What Patterns Trigger What Labels. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI'12), Toronto, Canada, 2012, pp.1012-1018. [code]
Yu-Feng Li and Zhi-Hua Zhou. Towards Making Unlabeled Data Never Hurt. In: Proceedings of the 28th International Conference on Machine Learning (ICML'11), Bellevue, WA, 2011, pp.1081-1088. [code]
Yu-Feng Li and Zhi-Hua Zhou. Improving Semi-Supervised Support Vector Machines through Unlabeled Instances Selection. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI'11), San Francisco, CA, 2011, pp.386-391. CORR abs/1005.1545
Yu-Feng Li, Sheng-Jun Huang, and Zhi-Hua Zhou, Regularized Semi-Supervsied Multi-Label Learning. In: Proceedings of the 4th Chinese Conference on Data Mining (CCDM'11) (in chinese with english abstract), 2011.
Yang Yu, Yu-Feng Li, and Zhi-Hua Zhou. Diversity Regularized Machine. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, pp.1603-1608. [code]
Yu-Feng Li, James T. Kwok, and Zhi-Hua Zhou. Cost-Sensitive Semi-Supervised Support Vector Machine. In: Proceedings of the 24th AAAI Conference on Artificial Intelligences (AAAI'10), Atlanta, GA, 2010, pp.500-505. [code]
Yu-Feng Li, James T. Kwok, Ivor W. Tsang, and Zhi-Hua Zhou. A Convex Method for Locating Regions of Interest with Multi-Instance Learning. In: Proceedings of the 20th European Conference on Machine Learning (ECML'09), Bled, Slovenia, 2009, pp.17-32. [code]
Yu-Feng Li, James T. Kwok, and Zhi-Hua Zhou. Semi-Supervised Learning using Label Mean. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.633-640. [code]
Yu-Feng Li, Ivor W. Tsang, James T. Kwok, and Zhi-Hua Zhou. Tighter and Convex Maximum Margin Clustering. In: Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS'09), Clearwater Beach, FL, 2009, pp.328-335. [code]
Zhi-Hua Zhou, Yu-Yin Sun, and Yu-Feng Li. Multi-Instance Learning by Treating Instances as Non-i.i.d. Samples. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.1249-1256. [code][data]
De-Chuan Zhan, Ming Li, Yu-Feng Li, and Zhi-Hua Zhou. Learning Instance Specific Distances using Metric Propagation. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.1225-1232. [code]