Yu-Feng Li  

 

Ph.D. Associate Researcher, LAMDA Group

Department of Computer Science and TechnologyNanjing University, China

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

 

Brief CV

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

Experience:


Research Interests

My current research interests mainly include Machine Learning and Data Mining.  More specifically, I am interested in:

Semi-supervised learning, multiple instance learning and multi-label learning

kernel methods, support vector machine and optimization

Interested Topics & Some Related Researchers


Publications [LAMDA Publications]

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. [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 《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). (Best student paper award, the 15th chinese conference on machine learning (CCML2015), Chengdu, China)

Yu-Feng Li and Zhi-Hua Zhou. Towards making unlabeled data never hurt. IEEE Transactions on Pattern Analysis and Machine Intelligence, 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, 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. 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.

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, 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. (Best Student Paper Award)

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]

[Google Scholar Citations]


Professional Activities

Organization Chair/Co-Chair:

Scholarship Chair:

PC Member:

Review for JMLR, TPAMI, MLJ, TKDE, TKDD, TNN, JCST, PR,《中国科学》,《自动化学报》 etc;

External Review for NIPS'13, ICML'13, AAAI'12, ICML'12, NIPS'11, ECML'11, ICML'11, NIPS'10, ICML'10, ECML'09, PAKDD'09;


Honers and Awards

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;


Talks

'Learning methods on safely using unlabeled data', ICMLC special session, Guangzhou, China, July.12-July.15, 2015

'Learning from large scale unlabelled data and categories', CIKM workshop, Shanghai, China, Nov.3-Nov.7, 2014

'Unlabeled data in social media and research on safely exploiting unlabeled data', The 3rd National Conference of Social Media Processing (SPM), Beijing, China, Nov.1-Nov.2, 2014

'Research on semi-supervised SVMs', The 5th Forum on CCF Outstanding Doctoral Dissertation, Xi'an, China, Jul.12~Jul.13, 2014

'Research on semi-supervised SVMs', The 11th China workshop on Machine learning and applications (MLA), Shanghai, China, Nov.2-Nov.3, 2013;


Courses and Teaching Assistant

Digital Image Processing. (For Undergraduate Students, Spring, 17)

Digital Image Processing. (For Undergraduate Students, Spring, 16)

Digital Image Processing. (For Undergraduate Students, Spring, 15)

Digital Image Processing. (For Undergraduate Students, Spring, 14)

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)


Students

I am very happy to work with the following people.

Master Students:

Shao-Bo Wang (2014.9-) (won the CCML15 and CCDM16 best student paper award; won Huawei Scholarship first prize 2015)

Hai Wang (2015.9-)

Wei Tong (2016.9-)

Undergraduate Students:

Hao Liu (2015.9-)

Graduated students:

Yuan-Zhao Li (2013.9-2016.6) (won the CCDM16 best student paper award; now at Baidu)

Han-Wen Zha (2014.9-2016.6) (now a phd student at UCSB)


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