个人简介
张敏灵 Min-Ling ZHANG
Professor
School of Computer Science and Engineering,
Key Laboratory of Computer Network and Information Integration of Ministry of Education (MOE),
Southeast University, China
Correspondence:
Min-Ling Zhang
School of Computer Science and Engineering
Southeast University
2 Sipailou
Nanjing 210096, China
Office:
528, Computer Building, Jiulonghu Campus of Southeast University
Currently, I am a professor at the PALM Group, School of Computer Science and Engineering, Southeast University. Before joining Southeast University, I worked as an assistant professor (2007.10~2010.5) at the College of Computer and Information Engineering, Hohai University.
I received my B.Sc., M.Sc., and Ph.D. degrees in computer science all from Department of Computer Science & Technology, Nanjing University, China, in 2001, 2004 and 2007 respectively. I was also a member of LAMDA group led by my supervisor Prof. Zhi-Hua Zhou.
For related information and resources, please navigate via the links in the left bar. Contact me if you have any problems there.
Activities
Action Editor Frontiers of Computer Science (Higher Education Press; 2019.01-)
Editorial Board
ACM Transactions on Intelligent Systems and Technology (ACM; 2017.05- )
Member
Neural Networks (Elsevier; 2017.01-2022.12)
Journal of Computer Science and Technology (Science Press / Springer; 2018.01- )
Science China Information Sciences (《中国科学: 信息科学》青年编委; 2018.01- )
Journal of Software (《软件学报》责任编委; 2019.01- )
Acta Automatica Sinica (《自动化学报》编委; 2015.09- )
Guest Editor
Machine Learning Journal special issue on Learning from Multi-Label Data
International Journal of Intelligence Science special issue on Data-Oriented Intelligence
Steering Committee Member ACML (Asian Conference on Machine Leanring, 2020- )
PAKDD (Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2019- )
General Co-Chair ACML'18 (The 10th Asian Conference on Machine Learning, Nov. 2018, Beijing, China)
Program Co-Chair CCDM'20 (The 8th China Conference on Data Mining, Aug. 2020, Changsha, China)
PAKDD'19 (The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Apr. 2019, Macau, China)
CCF-ICAI'19 (The 2nd CCF International Conference on Artificial Intelligence, Aug. 2019, Xuzhou, China)
MLA'19 (The 17th China Symposium on Machine Learning and Applications, Nov. 2019, Tianjin, China)
MLA'18 (The 16th China Symposium on Machine Learning and Applications, Nov. 2018, Nanjing, China)
ACML'17 (The 9th Asian Conference on Machine Learning, Nov. 2017, Seoul, Korea)
CCFAI'17 (The 2017 CCF Conference on Artificial Intelligence, Aug. 2017, Kunming, China)
PRICAI'16 (The 14th Pacific Rim International Conference on Artificial Intelligence, Aug. 2016, Phuket, Thailand)
Workshop Co-Chair
DFM'19 (The 1st ICDM Workshop on Dynamic Feature Mining, Nov. 2019, Beijing, China, in conjunction with ICDM 2019)
MLChina'14 (The ICML 2014 Workshop on Machine Learning in China, Jun. 2014, Beijing, in conjunction with ICML 2014)
LAWS'12 (The First International Workshop on Learning with Weak Supervision, Nov. 2012, Singapore, in conjunction with ACML 2012) [Working Notes]
MLD'10 (The Second International Workshop on Learning from Multi-Label Data, Jun. 2010, Haifa, Israel, in conjunction with ICML/COLT 2010) [Working Notes]
MLD'09 (The First International Workshop on Learning from Multi-Label Data, Sept. 2009, Bled, Slovenia, in conjunction with ECML/PKDD 2009) [Working Notes]
Proposal Reviewer
NWO (The Netherlands Organisation for Scientific Research)
NSFC (National Science Foundation of China)
Other Co-Chair Workshops Co-Chair (IJCAI'21, 30th International Joint Conference on Artificial Intelligence, August 2021, Montreal, Canada)
Tutorial Co-Chair (ICDM'20, 20th IEEE International Conference on Data Mining, November 2020, Sorrento, Italy)
Workshop Program Co-Chair (AAAI'20, 24th AAAI Conference on Artificial Intelligence, February 2020, New York, USA)
Workshop Program Co-Chair (AAAI'19, 23rd AAAI Conference on Artificial Intelligence, January 2019, Hawaii, USA)
Workshop Co-Chair (CIKM'19, 28th ACM International Conference on Information and Knowledge Management, November 2019, Beijing, China)
Tutorial Co-Chair (PRICAI'19, 16th Pacific Rim International Conference on Artificial Intelligence, August 2019, Cuvu, Fiji)
Publicity Co-Chair (ICDM'15, 15th IEEE International Conference on Data Mining, November 2015, Atlantic City, USA)
Workshop Co-Chair (ACML'14, 6th Asian Conference on Machine Learning, Nov. 2014, Nha Trang, Vietnam)
Student Volunteer Co-Chair (KDD'12, 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2012, Beijing, China)
Publication Chair (ACML'09, 1st Asian Conference on Machine Learning, Nov. 2009, Nanjing, China)
Tutorial Speaker
VALSE'16 (2016 Vision And Learning SEminar, Apr. 2016, Wuhan, China, with Dr. Xinggang Wang) [Slides: Multi-Instance Learning and Its Applications in Computer Vision]
ECML PKDD'09 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sept. 2009, Bled, Slovenia, with Dr. Grigorios Tsoumakas and Prof. Zhi-Hua Zhou) [Slides: Learning from Multi-label Data]
Invited Talks
ICMIP'20 (The 5th International Conference on Multimedia and Image Processing, January 2020, Nanjing, China. Title: "Research on Partial Label Learning") [Slides]
ICMLC'19 (The 11th International Conference on Machine Learning and Computing, February 2019, Zhuhai, China. Title: "Research on Partial Label Learning") [Slides]
IWPR'18 (2018 International Workshop on Pattern Recognition, May 2018, Jinan, China. Title: "Binary Relevance for Multi-Label Learning") [Slides]
CCML'19 (The 17th China Conference on Machine Learning, Aug. 2013, Kunming, China. Title: "Research on Partial Label Learning") [Slides]
CCML'13 (The 14th China Conference on Machine Learning, Aug. 2013, Kunming, China. Title: "Learning with Weak Supervision") [Slides]
MLA'10 (The 8th Chinese Workshop on Machine Learning and Applications, Nov. 2010, Nanjing, China. Title: "Learning from Multi-Label Data") [Slides]
JSAI 2011 (The 2011 Conference on Artificial Intelligence of Jiangsu Province, China, Oct. 2011, Xuzhou, China. Title: "Research on Multi-Label Learning") [Slides]
Senior Area Chair IJCAI'21 (30th International Joint Conference on Artificial Intelligence, August 2021, Montreal, Canada)
Area Chair AAAI'20 (24th AAAI Conference on Artificial Intelligence, February 2020, New York, USA)
ICDM'20 (20th IEEE International Conference on Data Mining, November 2020, Sorrento, Italy)
ECAI'20 (24th European Conference on Artificial Intelligence, June 2020, Santiago de Compostela, Spain)
ECML-PKDD'20 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2020, Ghent, Belgium)
AAAI'19 (23rd AAAI Conference on Artificial Intelligence, January 2019, Hawaii, USA)
ICDM'19 (19th IEEE International Conference on Data Mining, November 2019, Beijing)
ECML-PKDD'19 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2019, Würzburg, Germany)
IJCAI-ECAI'18 (27th International Joint Conference on Artificial Intelligence and 23rd European Conference on Artificial Intelligence, July 2018, Stockholm, Sweden)
ICDM'18 (18th IEEE International Conference on Data Mining, November 2018, Singapore)
ICPR'18 (24th International Conference on Pattern Recognition, August 2018, Beijing, China)
ICBK'18 (IEEE International Conference on Big Knowledge, November 2018, Singapore)
ICDM'17 (17th IEEE International Conference on Data Mining, November 2017, New Orleans, USA)
ICDM'16 (16th IEEE International Conference on Data Mining, December 2016, Barcelona, Spain)
ICDM'15 (15th IEEE International Conference on Data Mining, November 2015, Atlantic City, USA)
Senior PC Member
IJCAI-PRICAI'20 (29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence, July 2020, Yokohama, Japan)
ICONIP'20 (27th International Conference on Neural Information Processing, November 2020, Bangkok, Thailand)
IJCAI'19 (28th International Joint Conference on Artificial Intelligence, August 2019, Macau, China)
ACML'19 (11th Asian Conference on Machine Learning, Nov. 2019, Nagoya, Japan)
AAAI'18 (22nd AAAI Conference on Artificial Intelligence, February 2018, New Orleans, USA)
PAKDD'18 (22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, June 2018, Melbourne, Australia)
IJCAI'17 (26th International Joint Conference on Artificial Intelligence, August 2017, Melbourne, Australia)
AAAI'17 (21st AAAI Conference on Artificial Intelligence, February 2017, San Francisco, USA)
ACML'16 (8th Asian Conference on Machine Learning, Nov. 2016, Hamilton, New Zealand)
PAKDD'16 (20th Pacific-Asia Conference on Knowledge Discovery and Data Mining, April 2016, Auckland, New Zealand)
IJCAI'15 (24th International Joint Conference on Artificial Intelligence, July 2015, Buenos Aires, Argentina)
ACML'15 (7th Asian Conference on Machine Learning, Nov. 2015, Hong Kong, China)
PAKDD'15 (19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 2014, Ho Chi Ming City, Vietnam)
ACML'14 (6th Asian Conference on Machine Learning, Nov. 2014, Nha Trang, Vietnam)
IJCAI'13 (23rd International Joint Conference on Artificial Intelligence, August 2013, Beijing, China)
SDM'13 (2013 SIAM International Conference on Data Mining, May 2013, Austin, USA)
ACML'13 (5th Asian Conference on Machine Learning, Nov. 2013, Canberra, Australia)
ACML'12 (4th Asian Conference on Machine Learning, Nov. 2012, Singapore)
PC Member
2021: BigComp'21
2020: ICML'20, KDD'20, NeurIPS'20, AISTATS'20, IAAI'20, SDM'20, ICPR'20, IEEE TransAI'20, BigComp'20
2019: ICML'19, KDD'19, NeurIPS'19, AISTATS'19, SDM'19, PRICAI'19
2018: ICML'18, KDD'18, NIPS'18, SDM'18, AISTATS'18, ECML-PKDD'18, PAKDD'18, BigDataSE'18
2017: ICML'17, KDD‘17, NIPS'17, SDM'17, CIKM'17, AISTATS'17, ECML-PKDD'17, PAKDD'17, DSAA'17, ICMLC'17, ICBK'17, BigDataSE'17
2016: IJCAI'16, AAAI'16, KDD'16, ICML'16, NIPS'16, CIKM'16, ECML-PKDD'16, AISTATS'16, ICPR'16, CIDM'16, DSAA'16, BigComp'16, BigDataSE'16, BESC'16, IoP'16
2015: KDD'15, NIPS'15, ECML-PKDD'15, AISTATS'15, ICTAI'15, DSAA'15, ACIIDS'15, BigDataSE'15, HIBIBI'15, ICNC'15-FSKD'15
2014: ICML'14, ECML-PKDD'14 (Journal Track GEB), AISTATS'14, PAKDD'14, ECAI'14, ICPR'14, ASONAM'14, ICNC'14-FSKD'14, DS'14, ICONIP'14, ICTAI'14, TAAI'14
2013: AAAI'13, ECML-PKDD'13, MCS'13, ASONAM'13, CIDM'13, DS'13
2012: AAAI'12, ECML-PKDD'12, ECDM'12, ASONAM'12, SMC'12, HIS'12, ICMLA'12
2011: IJCAI'11, AAAI'11, KDD'11, ECML PKDD'11, ACML'11, IRI'11, SEKE'11, ICNC'11-FSKD'11, ICMLA'11, ICTAI'11, AI'11, ASONAM'11
2010: ICML'10, KDD'10, ACML'10, PRICAI'10, AI'10, ICPR'10, ICTAI'10, IRI'10, ECDM'10, ACSE'10, ASONAM'10, ICNC'10-FSKD'10, ICMLA'10, AC'10, ICHIT'10
2009: CIKM'09, ACML'09, AI'09, ICHIT'09, KDIR'09, KEOD'09, FCST'09, ICNC'09-FSKD'09, SEKE'09, ASONAM'09, IRI'09, IJCBS'09, AC'09
2008: PRICAI'08, ISKE'08, AI'08, ICMLA'08, ECDM'08, AC'08, ICHIT'08, KDD-DMBA'08 Workshop
2007: ICMLA'07
Journal Reviewer
Artificial Intelligence
Journal of Machine Learning Research
Machine Learning
Neural Computation
Data Mining and Knowledge Discovery
ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Database Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans
IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics
IEEE Transactions on Systems, Man and Cybernetics: Systems
IEEE Transactions on Cybernetics
IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Multimedia
IEEE Transactions on Industrial Electronics
IEEE Transactions on Industrial Informatics
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Audio, Speech and Language Processing
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Computational Social Systems
IEEE Transactions on Circuits and Systems II: Express Briefs
IEEE Transactions on Neural Systems and Rehabilitation Engineering
IEEE/ACM Transactions on Computational Biology and Bioinformatics
IEEE Intelligent Systems
Pattern Recognition
Neural Networks
AI Communications
European Journal of Operational Research
Pattern Recognition Letters
Computational Intelligence
Information Sciences
Knowledge and Information Systems
Knowledge-Based Systems
BMC Bioinformatics
Signal Processing
WIREs Data Mining and Knowledge Discovery
Journal of Computer Science and Technology
PLOS ONE
Journal of the Taiwan Institute of Chemical Engineers
Molecular BioSystems
Journal of Information Science and Engineering
International Journal of Approximate Reasoning
Neural Processing Letters
Information Processing Letters
Neurocomputing
Artificial Intelligence in Medicine
Applied Intelligence
Data and Knowledge Engineering
Intelligent Data Analysis
International Journal of Pattern Recognition and Artificial Intelligence
International Journal of Applied Mathematics and Computer Science
International Journal of Machine Learning and Cybernetics
International Journal of Data Science and Analytics
Computers & Mathematics with Applications
International Journal of Software and Informatics
EURASIP Journal on Advances in Signal Processing
Journal of Healthcare Engineering
Journal of Multiple-Valued Logic and Soft Computing
Journal of Parallel and Distributed Computing
International Journal of General Systems
Chinese Science Bulletin (科学通报)
Science in China Series F: Information Sciences (中国科学F辑: 信息科学)
Various Domestic Journals
COURSE
Name:
Software Engineering (软件工程)
To:
Undergraduate students
Semester: Fall 2017
Book:
Bernd Bruegge, Allen H. Dutoit. Object-Oriented Software Engineering: Using UML, Patterns and Java, 3rd edition, Prentice Hall, 2010.
(布吕格[美], 迪图瓦[美]. 面向对象软件工程：使用UML、模式与Java, 第三版, 清华大学出版社, 叶俊民,汪望珠等译, 2011.)
近期论文
查看导师最新文章
（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）
Journal Article
M.-L. Zhang, J.-P. Fang. Partial multi-label learning via credible label elicitation. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press. [Conference version] [code]
M.-L. Zhang, Q.-W. Zhang, J.-P. Fang, Y.-K. Li, X. Geng. Leveraging implicit relative labeling-importance information for effective multi-label learning. IEEE Transactions on Knowledge and Data Engineering, in press. [Conference version] [code]
B.-B Jia, M.-L. Zhang. Multi-dimensional classification via kNN feature augmentation. Pattern Recognition, in press. [Conference version] [code]
B.-B. Jia, M.-L. Zhang. Multi-dimensional classification via stacked dependency exploitation. Science China Information Sciences, in press. [code]
Y.-P. Sun, M.-L. Zhang. Compositional metric learning for multi-label classification. Frontiers of Computer Science, in press. [code]
Y. Zhang, Y. Wang, X.-Y. Liu, S. Mi, M.-L. Zhang. Large-scale multi-label classification using unknown streaming images. Pattern Recognition, in press.
M. Huang, F. Zhuang, X. Zhang, X. Ao, Z. Niu, M.-L. Zhang, Q. He. Supervised representation learning for multi-label classification. Machine Learning, 2019, 108(5): 747-763.
D. Zhou, Z. Zhang, M.-L. Zhang, Y. He. Weakly supervised POS tagging without disambiguation. ACM Transactions on Asian and Low-Resource Language Information Processing, 2018, 17(4): Article 35.
M.-L. Zhang, Y.-K. Li, X.-Y. Liu, X. Geng. Binary relevance for multi-label learning: An overview. Frontiers of Computer Science, 2018, 12(2): 191-202.
M.-L. Zhang, F. Yu, C.-Z. Tang. Disambiguation-free partial label learning. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(10): 2155-2167. [Conference version] [data] [code]
F. Yu, M.-L. Zhang. Maximum margin partial label learning. Machine Learning, 2017, 106(4): 573-593. [Conference version] [data] [code]
M.-L. Zhang, L. Wu. LIFT: Multi-label learning with label-specific features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 107-120. [Conference version] [code]
M.-L. Zhang, Z.-H. Zhou. A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819-1837. [Longer version]
M.-L. Zhang, Z.-H. Zhou. Exploiting unlabeled data to enhance ensemble diversity. Data Mining and Knowledge Discovery, 2013, 26(1): 98-129. [Conference version] [code]
Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, Y.-F. Li. Multi-instance multi-label learning. Artificial Intelligence, 2012, 176(1): 2291-2320. [code] (CORR abs/1005.1545)
M.-L. Zhang, Z.-H. Zhou. CoTrade: Confident co-training with data editing. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2011, 41(6): 1612-1626. [code]
M.-L. Zhang, J. M. Peña, V. Robles. Feature selection for multi-label naive bayes classification. Information Sciences, 2009, 179(19): 3218-3229. [code]
M.-L. Zhang, Z.-H. Zhou. Multi-instance clustering with applications to multi-instance prediction. Applied Intelligence, 2009, 31(1): 47-68. [code]
M.-L. Zhang, Z.-J. Wang. MIMLRBF: RBF neural networks for multi-instance multi-label learning. Neurocomputing, 2009, 72(16-18): 3951-3956. [code] [image data] [retuers data]
M.-L. Zhang. ML-RBF: RBF neural networks for multi-label learning. Neural Processing Letters, 2009, 29(2): 61-74. [code]
M.-L. Zhang, Z.-H. Zhou. ML-kNN: a lazy learning approach to multi-label learning. Pattern Recognition, 2007, 40(7): 2038-2048. [code] [Yeast data] [image data] [Yahoo data: original version preprocessed version]
Z.-H. Zhou, M.-L. Zhang. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems, 2007, 11(2): 155-170. [code]
M.-L. Zhang, Z.-H. Zhou. Multi-label neural networks with applications to functional genomics and text categorization. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(10): 1338-1351. [code] [Yeast data] [Reuters corpus]
M.-L. Zhang, Z.-H. Zhou. Adapting RBF neural networks to multi-instance learning. Neural Processing Letters, 2006, 23(1): 1-26. [code]
M.-L. Zhang, Z.-H. Zhou. Improve multi-instance neural networks through feature selection. Neural Processing Letters, 2004, 19(1): 1-10. [code] (11Kb) [TechReport for BP-MIP]
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Conference Paper
J.-H. Wu, X. Wu, Q.-G. Chen, Y. Hu, M.-L. Zhang. Feature-induced manifold disambiguation for multi-view partial multi-label learning. In: Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'20), San Diego, CA, 2020, in press. [code]
B.-B. Jia, M.-L. Zhang. Maximum margin multi-dimensional classification. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020, 4312-4319. [code]
Z.-S. Chen, X. Wu, Q.-G. Chen, Y. Hu, M.-L. Zhang. Multi-view partial multi-label learning with graph-based disambiguation. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 3553-3560. [code]
B.-B. Jia, M.-L. Zhang. MD-kNN: An instance-based approach for multi-dimensional classification. In: Proceedings of the 25th International Conference on Pattern Recognition (ICPR'20), Milan, Italy, in press. [code]
J.-H. Wu, M.-L. Zhang. Disambiguation enabled linear discriminant analysis for partial label dimensionality reduction. In: Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19), Anchorage, AK, 2019, 416-424. [code]
D.-B. Wang, L. Li, M.-L. Zhang. Adaptive graph guided disambiguation for partial label learning. In: Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19), Anchorage, AK, 2019, 83-91. [code]
X. Wu, Q.-G. Chen, Y. Hu, D.-B. Wang, X. Chang, X. Wang, M.-L. Zhang. Multi-view multi-label learning with view-specific information extraction. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macau, China, 2019, 3884-3890. [code]
Z.-S. Chen, M.-L. Zhang. Multi-Label learning with regularization enriched label-specific features. In: Proceedings of the 11th Asian Conference on Machine Learning (ACML'19), Nagoya, Japan, 2019, 411-424.
Y. Zhang, W.-P. Fan, X. Wu, H. Chen, B.-Y. Li, M.-L. Zhang. CAFE: Adaptive VDI workload prediction with multi-grained features. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019, 5821-5828.
B.-B. Jia, M.-L. Zhang. Multi-dimensional classification via kNN feature augmentation. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019, 3975-3982. [code]
J.-P. Fang, M.-L. Zhang. Partial multi-label learning via credible label elicitation. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019, 3518-3525. [code]
J. Wang, M.-L. Zhang. Towards mitigating the class-imbalance problem for partial label learning. In: Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'18), London, UK, 2018, 2427-2436. [code]
S.-Y. Ding, X.-Y. Liu, M.-L. Zhang. Imbalanced augmented class learning with unlabeled data by label confidence propagation. In: Proceedings of the 18th IEEE International Conference on Data Mining (ICDM'18), Singapore, 2018, 79-88.
X. Wu, M.-L. Zhang. Towards enabling binary decomposition for partial label learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, 2868-2974. [code]
Q.-W. Zhang, Y. Zhong, M.-L. Zhang. Feature-induced labeling information enrichment for multi-label learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018, 4446-4453. [code]
W. Zhan, M.-L. Zhang. Inductive semi-supervised multi-label learning with co-training. In: Proceedings of the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'17), Halifax, Canada, 2017, 1305-1314. [code]
W.-J. Zhou, Y. Yu, M.-L. Zhang. Binary linear compression for multi-label classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, 3546-3552.
C.-Z. Tang, M.-L. Zhang. Confidence-rated discriminative partial label learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017, 2611-2617. [data] [code]
M.-L. Zhang, B.-B. Zhou, X.-Y. Liu. Partial label learning via feature-aware disambiguation. In: Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'16), San Francisco, CA, 2016, 1335-1344. [data] [code]
P. Hou, X. Geng, M.-L. Zhang. Multi-label manifold learning. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016, 1680-1686. [code]
F. Yu, M.-L. Zhang. Maximum margin partial label learning. In: Proceedings of the 7th Asian Conference on Machine Learning (ACML'15), Hong Kong, China, 2015, 96-111. [data] [code]
M.-L. Zhang, F. Yu. Solving the partial label learning problem: An instance-based approach. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, 4048-4054. [data] [code]
M.-L. Zhang, Y.-K. Li, X.-Y. Liu. Towards class-imbalance aware multi-label learning. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, 4041-4047. [code]
Y.-K. Li, M.-L. Zhang, X. Geng. Leveraging implicit relative labeling-importance information for effective multi-label learning. In: Proceedings of the 15th IEEE International Conference on Data Mining (ICDM'15), Atlantic City, NJ, 2015, 251-260. [code]
M.-L. Zhang. Disambiguation-free partial label learning. In: Proceedings of the 14th SIAM International Conference on Data Mining (SDM'14), Philadelphia, PA, 2014, 37-45. [data] [code]
L. Wu, M.-L. Zhang. Multi-label classification with unlabeled data: An inductive approach. In: Proceedings of the 5th Asian Conference on Machine Learning (ACML'13), Canberra, Australia, 2013, 197-212. [code]
M.-L. Zhang. LIFT: Multi-label learning with label-specific features. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, 1609-1614. (poster) [code]
M.-L. Zhang, Z.-H. Zhou. Exploiting unlabeled data to enhance ensemble diversity. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM'10), Sydney, Australia, 2010, 619-628. [code] (CORR abs/0909.3593)
M.-L. Zhang, K. Zhang. Multi-label learning by exploiting label dependency. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'10), Washington D. C., 2010, 999-1007. [code]
M.-L. Zhang. A k-nearest neighbor based multi-instance multi-label learning algorithm. In: Proceedings of the 22nd International Conference on Tools with Artificial Intelligence (ICTAI'10), Arras, France, 2010, 207-212. [code]
M.-L. Zhang. Generalized multi-instance learning: problems, algorithms and data sets. In: Proceedings of the 2009 Global Congress on Intelligent Systems, vol. III (GCIS'09), Xiamen, China, 2009, 539-543.
M.-L. Zhang, Z.-H. Zhou. M3MIML: A maximum margin method for multi-instance multi-label learning. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, 688-697. [code] [image data] [retuers data]
M.-L. Zhang, Z.-H. Zhou. Multi-label learning by instance differentiation. In: Proceedings of the 22nd Conference on Artificial Intelligence (AAAI'07), Vancouver, Canada, 2007, 669-674. [code]
Z.-H. Zhou, M.-L. Zhang. Multi-instance multi-label learning with application to scene classification. In: Advances in Neural Information Processing Systems 19 (NIPS'06), Vancouver, Canada, 2007, 1609-1616. [code] [image data]
M.-L. Zhang, Z.-H. Zhou. A k-nearest neighbor based algorithm for multi-label classification. In: Proceedings of the 1st IEEE International Conference on Granular Computing (GrC'05), Beijing, 2005, 718-721. [code]
M.-L. Zhang, Z.-H. Zhou. Ensembles of multi-instance neural networks. In: Proceedings of the International Conference on Intelligent Information Processing (ICIIP'04), Beijing, 2004, 471-474.
Z.-H. Zhou, M.-L. Zhang. Ensembles of multi-instance learners. In: Lavrač N, Gamberger D, Blockeel H, Todorovski L, Eds. Lecture Notes in Artificial Intelligence 2837 (ECML'03), Berlin: Springer-Verlag, 2003, 492-502. [code]
Z.-H. Zhou, M.-L. Zhang, Chen K-J. A novel bag generator for image database retrieval with multi-instance learning techniques. In: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03), Sacramento, CA, 2003, 565-569.
Z.-H. Zhou, M.-L. Zhang. Neural networks for multi-instance learning. In: Proceedings of the International Conference on Intelligent Information Technology (ICIIT'02), Beijing, 2002, 455-459.
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