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Ming Li's Selected Publication List


[Journal Articles]
W. Zheng, H.-Y. Zhou, M. Li, and J. Wu. CodeAttention: Translating source code to comments by exploiting the code constructs. Frontiers of Computer Science, in press.
M. Li, H. Zhang, R. Wu, and Z.-H. Zhou. Sample-based software defect prediction with active and semi-supervised learning. Automated Software Engineering, 2012, 19(2): 201-230.
Y. Jiang, M. Li, and Z.-H. Zhou. Software defect detection with ROCUS. Journal of Computer Science and Technology, 2011, 26(2): 328-342.
Z.-H. Zhou and M. Li. Semi-supervised learning by disagreement. Knowledge and Information Systems, 2010, 24: 415-439.
M. Li, W. Wang, and Z.-H. Zhou. Exploiting remote learners in internet environment with agents. Science in China - F: Information Sciences, 2010, 53(1): 64-76
M. Li, H. Li, and Z.-H. Zhou. Semi-supervised document retrieval. Information Processing & Management, 2009, 45(3): 341-355.
Y. Jiang, M. Li, and Z.-H. Zhou. Mining extremely small data sets with application to software reuse. Software: Practice and Experience, 2009, 39(4): 423-440.
Z.-H. Zhou and M. Li. Semi-supervised regression with co-training style algorithms. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(11): 1479-1493.
M. Li and Z.-H. Zhou. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans, 2007, 37(6): 1088-1098.
Y. Yu, D.-C. Zhan, X.-Y. Liu, M. Li, and Z.-H. Zhou. Predicting future customers via ensembling gradually expanded trees. International Journal of Data Warehousing and Mining, 2007, 3(2): 12-21. Invited paper for the PAKDD'06 Data Mining Competition (Open Category) Grand Champion Team
Z.-H. Zhou and M. Li. Tri-training: exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(11): 1529-1541.
Z.-H. Zhou, K. Jiang, and M. Li. Multi-instance based web mining. Applied Intelligence, 2005, 22(2): 135-147.
L. Han and M. Li Open source software classification using cost-sensitive multi-label learning. Journal of Software, 2014, 25(9): 1982-1991. (in Chinese with English abstract)
M. Li and Z.-H. Zhou. Online semi-supervised learning with multi-kernel ensemble. Journal of Computer Research and Development, 2008, 45(12): 2060-2068. (in Chinese with English abstract) Invited paper for the 50th anniversary of Journal of Computer Research and Development.
M. Li, X.-B. Xue, and Z.-H. Zhou. Chinese web index page recommendation based on multi-instance learning. Journal of Software, 2004, 15(9): 1328-1335. (in Chinese with English abstract)


[Conference Papers]
Y.-Y. Zhang and M. Li. Find me if you can: Deep software clone detection by exploiting the contest between the plagiarist and the detector. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.
S.-T. Shi, M. Li,, D. Lo, F. Thung, and X. Huo. Automatic code review by learning the revision of source code. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.
Y.-D. Zhang, W. Zheng, and M. Li. Learning uniform semantic features for natural language and programming language globally, locally and sequentially. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.
Z.-Y. Shen and M. Li. T2S: Domain adaptation via model-independent inverse mapping and model reuse. In: Proceedings of the 18th International Conference on Data Mining (ICDM'18), Singapore, 2018, pp.1224-1229.
X. Huo, Y. Yang, M. Li., and D.-C. Zhan. Learning semantic features for software defect prediction by code comments Embedding.. In: Proceedings of the 18th International Conference on Data Mining (ICDM'18), Singapore, 2018, pp.1049-1054.
Y.-J. Wang, Y. Yao, H.-H. Tong, X. Huo, M. Li., F. Xu, and J. Lu. Bug localization via supervised topic modeling.. In: Proceedings of the 18th International Conference on Data Mining (ICDM'18), Singapore, 2018, pp.607-616.
Z. Xie and M. Li. Cutting the software building efforts in continuous integration by semi-supervised online AUC optimization. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.2875-2881.
H.-H. Wei and M. Li. Positive and unlabeled learning for detecting software functional clones with adversarial training. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.2840-2846.
Z. Xie and M. Li. Semi-supervised AUC optimization without guessing labels of unlabeled data. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018, pp.4310-4317.
A.-S. Ni and M. Li. ACONA: active online model adaptation for predicting continuous integration build failures. In: Proceedings of the 40th International Conference on Software Engineering: Companion Volume (ICSE'18), Gothenburg, Sweden, 2018, pp.366-367.
X. Huo and M. Li. Enhancing the unified features to locate buggy files by exploiting the sequential nature of source code. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, 1909-1915.
H.-H. Wei and M. Li. Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, 3034-3040.
A.-S. Ni and M. Li. Cost-effective build outcome prediction using cascaded classifiers. In: Proceedings of the 14th International Conference on Mining Software Repositories (MSR'17), Buenous Aires, Argentina, 2017.
X. Huo, M. Li, and Z.-H. Zhou. Learning unified features from natural and programming languages for locating buggy source code. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), New York, NY, 2016, 1606-1612.
T.-D. Le, D. Lo and M. Li. Constrained feature selection for localizing faults. In: Proceedings of the 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME'15), Bremen, Germany, 2015, pp. 501-505.
T. Li and M. Li. PerGrab: Adapting grabbing gesture recognition for personalized non-contact HCI. In: Proceedings of the 2013 International Conference on Intelligence Science and Big Data Engineering (IScIDE'13), Beijing, China, pp. 740-747.
M. Li, W.-W Tu, C.-Y. Fan, and Z.-H. Zhou. Cost sensitive semi-supervised defect prediction. In: Working Notes of the ACML 2012 Workshop on Learning with Weak Supervision (LWS'12), Singapore, 2012, pp.45-59.
M. Li, X.-B. Xue, and Z.-H. Zhou. Exploiting multi-modal interactions: A unified framework. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp. 1120-1125.
D.-C. Zhan, M. Li, Y.-F. Li, and Z.-H. 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.
A. B. Goldberg, M. Li, and X. Zhu. On manifold regularization: A new learning setting and empirical study. In: Proceedings of the 19th European Conference on Machine Learning (ECML'08), LNAI 5211, Antwerp, Belgium, 2008, pp. 393-407.
M. Li, Z. Zhang, and Z.-H. Zhou. Mining bulletin board systems using community generation. In: Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'08), Osaka, Japan, LNAI 5012, 2008, pp.209-221.
Y. Jiang, M. Li, and Z.-H. Zhou. Generation of comprehensible hypotheses from gene expression data. In: Proceedings of the International Workshop on Data Mining for Biomedical Application (BioDM'06), in conjunction with PAKDD'06, Singapore, LNBI 3916, 2006, pp.116-123.
Z.-H. Zhou and M. Li. Semi-supervised regression with co-training. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05), Edinburgh, Scotland, 2005, pp.908-913.
M. Li and Z.-H. Zhou. SETRED: self-training with editing. In: Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'05), Hanoi, Vietnam, LNAI 3518, 2005, pp.611-621.

[Thesis]
M. Li. Research on single-view co-training approaches. Ph. D. Dissertation. Department of Computer Science and Technology, Nanjing University, 2008. (in Chinese with English abstract)