Yu-Feng Li  

 

Ph.D. Associate Professor, LAMDA Group

Department of Computer Science and Technology, Nanjing University, China

Email: liyf(at)nju(dot)edu(dot)cn

Brief CV

I am an associate professor of Department of Computer Science and Technology in Nanjing University and a faculty member of LAMDA Group, led by professor Zhi-Hua Zhou. I received my B.Sc. and Ph.D. degree in Computer Science of Nanjing University in June 2006 and June 2013, respectively. [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 data and others

Interested Topics & Some Related Researchers


Students

I am very happy to work with the following people.

Ph.D. Students:

Wei Tong (2016.9-) (won the first prize of Huawei Scholarship, 2017; won Artificial Intelligence Scholarship 2018)

Lan-Zhe Guo (2017.9-)(won national scholarship 2018)

Master Students:

De-Ming Liang (2017.9-)(won national scholarship 2018); Qian-Wei Wang (2017.9-) (co-supervised with Prof. Zhi-Hua Zhou); Feng Shi (2018.9-); Yong-Nan Zhu (2018.9-)

Graduated students:

Hao-Chen Dong (2016.9-2019.6) (one AAAI18 paper; co-supervised with Prof. Zhi-Hua Zhou); Hai Wang (2015.9-2018.6) (Master; one PRICAI16 paper and one FCS18 paper; won the first prize of academic scholarship of Nanjing Unversity, 2016; now at 4 Paradigm); Hao Liu (2015.9-2018.6) (Bachelor; now a phd student at Caltech); Shao-Bo Wang (2014.9-2017.6) (Master; one IJCAI16 paper; won the CCML15 and CCDM16 best student paper award; won national scholarship 2016; won the first prize of Huawei Scholarship 2015; now at Microsoft Suzhou); Han-Wen Zha (2014.9-2016.6) (Bachelor; one AAAI17 paper; now a phd student at UCSB); Yuan-Zhao Li (2013.9-2016.6) (Master; won the CCDM16 best student paper award; now at Baidu)


Publications(*indicate my student) [LAMDA Publications][Google Scholar Citations]

Yu-Feng Li, Lan-Zhe Guo (co-first author), Zhi-Hua Zhou. Towards Safe Weakly Supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), in press. [code]

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. [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.

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.

Yu-Feng Li, De-Ming Liang. Safe Semi-Supervised Learning: A Brief Introduction. Frontiers of Computer Science (FCS). 2019, 13(4): 669-676.

Miao Xu, Yu-Feng Li, Zhi-Hua Zhou. Robust Multi-Label Learning with PRO Loss. IEEE Transactions on Knowledge and Data Engineering (TKDE). in press.

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. [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. [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.

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.

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.

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.

Yu-Feng Li and Zhi-Hua Zhou. Research on Semi-Supervised SVMs. Book Chapter of 'Machine Learning and its Applications 2015'《机器学习及其应用2015》

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).

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.

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]

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 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]

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.

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]


Services

Conference/Workshop/Tutorial/Special Session/Symoposium

Journal:

Conference:

Professional Organization:


Honers and Awards

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 Machine Learning (Spring, 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)


Correspondence

Address:

Yu-Feng Li

National Key Laboratory for Novel Software Technology;

163 Xianlin Avenue, Qixia District, Nanjing 210023, China;

Nanjing Univeristy Xianlin Campus Mailbox 603;

919, Computer Science Building