李宇峰 Yu-Feng Li  

南京大学人工智能学院 教授,博导

 

Ph.D. Professor, LAMDA Group

National Key Laboratory for Novel Software Technology, Nanjing University, China

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

Brief CV

I am a professor of the School of Artificial Intelligence, National Key Laboratory for Novel Software Technology in Nanjing University and a faculty member of LAMDA Group, led by professor Zhi-Hua Zhou.


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]

Software

LAMDA-SSL: We provide a comprehensive and easy-to-use toolkit for semi-supervised learning. LAMDA-SSL contains 30+ semi-supervised learning algorithms, including both statiscal and deep semi-supervised learning.

LaWGPT: We open-sourced a LLM for Chinese Legal domain.

 

Track Chapter

ACML 2022 Journal Track; Guest Editors: Yu-Feng Li, Prateek Jain, Machine Learning Journal 2023

ACML 2021 Journal Track; Guest Editors: Yu-Feng Li, Mehmet Gonen, Kee-Eung Kim, Machine Learning Journal 2022

 

Book Chapter

Min-Ling Zhang, Qing-Hua Hu and Yu-Feng Li. Machine Learning and Its Applications. Editor, 2021

Yu-Feng Li and Zhi-Hua Zhou. Research on Semi-Supervised SVMs. Book Chapter of 'Machine Learning and its Applications' 2015

 

Journal Paper

21. Weikai Yang, Yukai Guo, Jing Wu, Zheng Wang, Lan-Zhe Guo, Yu-Feng Li, Shixia Liu. Interactive Reweighting for Mitigating Label Quality Issues. IEEE Transactions on Visualization and Computer Graphics, 30(3): 1837-1852, 2024.

20. Jiang-Xin Shi*, Tong Wei, Yu-Feng Li. Residual Diverse Ensemble for Long-Tailed Multi-Label Text Classification. Science CHINA Information Science, In Press.

19. Zhi Zhou*, Yi-Xuan Jin, Yu-Feng Li. RTS: Learning Robustly from Time Series Data with Noisy Label. Frontiers of Computer Science, 2024, 18(6): 186332.

18. Zhi Zhou*, Ding-Chu Zhang, Yu-Feng Li, Min-Ling Zhang. Towards Robust Test-Time Adaptation for Open-Set Recognition. Journal of Software (软件学报), 35(4), 2024.

17. Lin-Han Jia*, Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li. LAMDA-SSL: A Comprehensive Semi-Supervised Learning Toolkit. Science CHINA Information Science, 2024, 67: 117101.

16. Lan-Zhe Guo*, Yu-Feng Li. 稳健选择伪标注的混合式半监督学习. Science CHINA Information Science (In chinese), 2024, 54(3): 623�637. DOI: 10.1360/SSI-2022-0421.

15. Tong Wei*, Hai Wang, Wei-Wei Tu, Yu-Feng Li. Robust Model Selection for PU Learning under Constraint. Science CHINA Information Science, 65: 212101, 2022.

14. 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

13. 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.

12. 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]

11. 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.

10. 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.

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

8. 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]

7. 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.

6. 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]

5. 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]

4. 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]

3. 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.

2. 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]

1. 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.

 

Conference Paper

66. Jie-Jing Shao*, Han-Sen Shi, Lan-Zhe Guo, Yu-Feng Li. Offline Imitation Learning with Model-based Reverse Augmentation. In:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'24), 2024.

65. Jiang-Xin Shi*, Chi Zhang, Tong Wei, Yu-Feng Li. Efficient and Long-Tailed Generalization for Pre-trained Vision-Language Model. In:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'24), 2024.

64. Xiao-Wen Yang*, Wen-Da Wei, Jie-Jing Shao, Yu-Feng Li, Zhi-Hua Zhou. Analysis for Abductive Learning and Neural-Symbolic Reasoning Shortcuts. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24). 2024.

63. Zhi Zhou*, Ming Yang, Jiang-Xin Shi, Lan-Zhe Guo, Yu-Feng Li. DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24). 2024.

62. Jiang-Xin Shi*, Tong Wei, Zhi Zhou, Jie-Jing Shao, Xin-Yan Han, Yu-Feng Li. Long-tail Learning with Foundation Model: Heavy Fine-tuning Hurts. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24). 2024.

61. Heng-Kai Zhang*, Yi-Ge Zhang, Zhi Zhou, Yu-Feng Li. LSPAN: Spectrally Localized Augmentation for Graph Consistency Learning. In: Proceedings of the 33th International Joint Conference on Artificial Intelligence (IJCAI'24), 2024.

60. Lin-Han Jia*, Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li. A Benchmark on Robust Semi-Supervised Learning in Open Environments. In: Proceedings of the 12th International Conference on Learning Representations (ICLR'24), 2024.

59. Xiao-Wen Yang*, Jie-Jing Shao, Wei-Wei Tu, Yu-Feng Li, Wang-Zhou Dai, Zhi-Hua Zhou. Safe Abductive Learning in the Presence of Inaccurate Rules. In: Proceedings of the 38th AAAI conference on Artificial Intelligence (AAAI'24), 2024.

58. Ding-Chu Zhang*, Zhi Zhou, Yu-Feng Li. Robust Test-Time Adaptation for Zero-Shot Prompt Tuning. In: Proceedings of the 38th AAAI conference on Artificial Intelligence (AAAI'24), 2024.

57. Heng-Kai Zhang*, Yi-Ge Zhang, Zhi Zhou, Yu-Feng Li. HONGAT: Graph Attention Networks in the Presence of High-Order Neighbors. In: Proceedings of the 38th AAAI conference on Artificial Intelligence (AAAI'24), 2024.

56. Jiang-Xin Shi*, Tong Wei, Yuke Xiang, Yu-Feng Li. How Re-sampling Helps for Long-Tail Learning? In: Advances in Neural Information Processing Systems (NeurIPS'23), 2023.

55. Lan-Zhe Guo*, Zhi Zhou, Yu-Feng Li, Zhi-Hua Zhou. Identifying Useful Learnwares for Heterogeneous Label Spaces. In: Proceedings of the 40th International Conference on Machine Learning (ICML'23). 2023.

54. Zhi-Zhou*, Lan-Zhe Guo, Lin-Han Jia, Ding-Chu Zhang, Yu-Feng Li. ODS: Test-Time Adaptation in the Presence of Open-World Data Shift. In: Proceedings of the 40th International Conference on Machine Learning (ICML'23). 2023.

53. Lin-Han Jia*, Lan-Zhe Guo, Zhi Zhou, Jie-Jing Shao, Yuke Xiang, Yu-Feng Li. Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions. In: Proceedings of the 40th International Conference on Machine Learning (ICML'23). 2023.

52. Jie-Jing Shao*, Lan-Zhe Guo, Xiao-Wen Yang, Yu-Feng Li. LOG: Active Model Adaptation for Label-Efficient OOD Generalization.. In: Advances in Neural Information Processing Systems (NeurIPS'22), Virtual Conference, 2022.

51. Lan-Zhe Guo*, Yi-Ge Zhang, Zhi-Fan Wu, Jie-Jing Shao, Yu-Feng Li. Robust Semi-Supervised Learning when Not All Classes have Labels.. In: Advances in Neural Information Processing Systems (NeurIPS'22), Virtual Conference, 2022.

50. Yidong Wang, Hao Chen, Yue Fan, Wang SUN, Ran Tao, Wenxin Hou, Renjie Wang, Linyi Yang, Zhi Zhou, Lan-Zhe Guo, Heli Qi, Zhen Wu, Yu-Feng Li, Satoshi Nakamura, Wei Ye, Marios Savvides, Bhiksha Raj, Takahiro Shinozaki, Bernt Schiele, Jindong Wang, Xing Xie, Yue Zhang. USB: A Unified Semi-supervised Learning Benchmark for Classification.. In: Advances in Neural Information Processing Systems (NeurIPS'22), Virtual Conference, 2022.

49. Lan-Zhe Guo*, Yu-Feng Li. Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding. In: Proceedings of the 39th International Conference on Machine Learning (ICML'22). 2022.

48. Jie-Jing Shao*, Yunlu Xu, Zhanzhan Cheng, Yu-Feng Li. Active Model Adaptation Under Unknown Shift. In:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), 2022.

47. Tong Wei*, Jiang-Xin Shi*, Yu-Feng Li, Min-Ling Zhang. Prototypical Classifier for Robust Class-Imbalanced Learning. In: Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'22). Chengdu, China. 2022.

46. 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), Virtual Conference, 2021.

45. 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.

44. 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.

43. 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

42. 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

41. 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

40. 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.

39. 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.

38. 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.

37. 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.

36. 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.

35. 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]

34. 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]

33. 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.

32. 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]

31. 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.

30. 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]

29. 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.

28. 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]

27. 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]

26. 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]

25. 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.

24. 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]

23. 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]

22. 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.

21. 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]

20. 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]

19. 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]

18. 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.

17. 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]

16. 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.

15. 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.

14. 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.

13. 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.

12. 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.

11. 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]

10. 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]

9. 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

8. 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.

7. 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]

6. 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]

5. 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]

4. 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]

3. 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]

2. 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]

1. 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 Committee:

Journal:

Workshop Organization:

Professional Organization:


Students

I am very happy to work with the following people.

Ph.D. Students:

Jie-Jing Shao (2022.9-; where 2019.9-2022.6 studying for master degree)(won National Scholarship 2021; won LAMDA excellent student award runner-up 2021);

Zhi Zhou (2022.9-; where 2020.9-2022.6 studying for master degree)(won AI scholarship 2021; won LAMDA excellent student award 2022);

Jiang-Xin Shi (2023.9-; where 2020.9-2023.6 studying for master degree)

Lin-Han Jia (2022.9-)

Xiao-Wen Yang (2023.9-)

Master Students (Sort alphabetically by last name for persons of the same grade):

Xin-Yan Han (2021.9-); Yi-Xuan Jin (2021.9-); Qian-Yu Liu (2021.9-); Yi-Ge Zhang (2021.9-); Zhao-Long Li (2022.9-); Yu-Zhe Ma (2022.9-); Hao-Sen Shi (2022.9-); Peng-Xiao Song (2022.9-); Heng-Kai Zhang (2022.9-); Si-Yu Han (2023.9-); Wen Tao (2023.9-); Chen-Xi Zhang (2023.9-); Ding-Chu Zhang (2023.9-); Jia Zhang (2023.9-);

Graduated students (Sort alphabetically by last name for persons of the same grade):

Lan-Zhe Guo (2017.9-2022.6)(won National Scholarship 2018; won Artificial Intelligence Scholarship 2019; won National Scholarship 2020; won Baidu Scholarship 2021; won Microsoft Scholarship runner-up 2021; Now assistant professor at Nanjing University)

Wei Tong (2016.9-2021.12) (won the first prize of Huawei Scholarship, 2017; won Artificial Intelligence Scholarship 2018; won National Scholarship 2019; Now Candidate Associate Professor at Southeast University);

Tao Han (2019.9-2022.6)(One AAAI21 paper; Now a phd student at HKU);

Zhi-Fan Wu (2019.9-2022.6) (One ICCV21 paper; Now at alibaba);

Feng Shi (2018.9-2021.6)(won National Scholarship 2019; Now at Huawei);

Yong-Nan Zhu (2018.9-2021.6)(won National Scholarship 2020; Now at Alibaba Group);

De-Ming Liang (2017.9-2020.6)(won National Scholarship 2018; won Huawei Scholarship 2019; Now at Huawei);

Qian-Wei Wang (2017.9-2020.6) (co-supervised with Prof. Zhi-Hua Zhou; won Artificial Intelligence Scholarship 2019; Now a phd student at Tsinghua University (Shenzhen));

Hao-Chen Dong (2016.9-2019.6) (Master; one AAAI18 paper; co-supervised with Prof. Zhi-Hua Zhou; Now at Xiong'an Economic Development Zone);

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)


Honers and Awards

Pacific-Asia on Knowledge Discovery and Data Mining, Early-Career Research Award, 2024.

吴文俊人工智能科技奖: 优秀博士学位论文指导教师 Supervior for Excellent PhD Thesis (Lan-Zhe Guo), China Association of Artificial Intelligence

江苏省人工智能学会优秀博士学位论文指导教师 Supervior for Excellent PhD Thesis (Tong Wei), Jiangsu Association of Artificial Intelligence

华为"揭榜挂帅"火花奖 HUAWEI Spark Award

国家“万人计划”青年拔尖人才 The National Youth Talent Program

亚太国际数据挖掘会议最佳论文奖 PAKDD 2022 Best Paper Award

IJCAI 2021 优秀青年学者亮点报告 IJCAI 2021 Early-Career Spotlight Talk

指导学生获百度奖学金 Supervior for Baidu Scholarship (Lan-Zhe Guo), 2021

指导学生获微软学者提名奖 Supervior for Microsoft Scholarship runner-up (Lan-Zhe Guo), 2021

中国计算机学会优博 CCF Outstanding Doctoral Dissertation Award;

中国数据挖掘会议最佳学生论文奖 CCDM Best Student Paper Award;

微软学者奖 Microsoft Fellowship Award;


Courses and Teaching Assistant

Introduction to Advanced Machine Learning (For graduate students, Spring 2024; Spring 2023; Spring 2022; Spring 2021)

Matrix Computation (For undergraduate students, Spring 2024; Spring 2023;)

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, 2014)

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;

306, Computer Science Building