楊海欽博士 YANG Haiqin
Dr YANG Haiqin (楊海欽博士)
BSc (Nanjing University)
MPhil (CUHK)
PhD (CUHK)

Adjunct Assistant Professor
Department of Computing

Email : hyang@hsmc.edu.hk

Dr Yang received his BSc Degree from the Department of Computer Science, Nanjing University, and his MPhil and PhD degrees from the Department of Computer Science and Engineering, The Chinese University of Hong Kong (CUHK).  Before joining HSMC, he was a Postdoctoral Fellow at CUHK.

Dr Yang’s research interests include machine learning, data mining, and big data analytics.  He has published two books and over 40 technical publications in journals and conferences in his areas of expertise. Dr Yang has initiated and co-organized five international workshops on the topic of scalable machine learning and scalable data analytics. He has served as an associate editor of Neurocomputing, a program committee member and a reviewer of over twenty top-tier conferences and journals.


Newspaper ArticlesPublicationsResearch InterestsTeaching AreasMajor discipline(s)
TopNewspaper Articles

Selected Conference Presentations

  1. Shenglin Zhao, Tong Zhao, Haiqin Yang, Michael R. Lyu, Irwin King. STELLAR: Spatial-Temporal Latent Ranking for Successive Point-of-Interest Recommendation. In AAAI, 2016.
  2. Xixian Chen, Haiqin Yang, Irwin King, and Michael R. Lyu. Training-Efficient Feature Map for Shift-Invariant Kernels. In IJCAI, pages 3395-3401, 2015.
  3. Junjie Hu, Haiqin Yang, Irwin King, Michael R. Lyu, and Anthony Man-Cho So. Kernelized Online Imbalanced Learning with Fixed Budgets. In AAAI, pages 2666-2672, 2015.
  4. Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Non-Monotonic Feature Selection for Regression. In ICONIP (2): 44-51, 2014.
  5. Chenxia Wu, Haiqin Yang, Jianke Zhu, Jiemi Zhang, Irwin King, and M.R. Lyu. Sparse Poisson Coding for High Dimensional Document Clustering. In IEEE BigData 2013, pages 512-517, 2013.
  6. Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. Where You Like to Go Next: Successive Point-of-Interest Recommendation. In IJCAI, Beijing, China, August 3-9, 2013.
  7. Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. Fused Matrix Factorization with Geographical and Social Influence in Location-based Social Networks. In AAAI, Toronta, Canada, 2012.
  8. Guang Ling, Haiqin Yang, Michael R. Lyu, and Irwin King. Response Aware Model-Based Collaborative Filtering. In UAI, pages 501–510, Catalina Island, USA, August 15-17, 2012.
  9. Haiqin Yang, Shenghuo Zhu, Irwin King, and Michael R. Lyu. Can Irrelevant Data Help Semi-supervised Learning, Why and How? In CIKM, pages 937–946, 2011.
  10. Haiqin Yang, Irwin King, and Michael R. Lyu. Online Learning for Multi-Task Feature Selection. In CIKM, pages 1693–1696, 2010.
  11. Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Online Learning for Group Lasso. In ICML2010, pages 1191–1198, 2010.
  12.  Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, and Michael R. Lyu. Simple and Efficient Multiple Kernel Learning by Group Lasso. In ICML2010, pages 1175–1182, 2010.

 

TopPublications

Books

  1. Haiqin Yang, Irwin King, and Michael R. Lyu. Sparse Learning under Regularization Framework: Theory and Applications. LAP Lambert Academic Publishing, 2011.
  2.  Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu. Machine Learning: Modeling Data Locally and Globally. Advanced Topics in Science and Tecnology in China: Machine Learning. Zhejiang University Press with Springer Verlag, 2008.

 

Journal Articles

  1. Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. A Unified Point-of-interest Recommendation Framework in Location-based Social Networks. ACM Transactions on Intelligent Systems and Technology (TIST). Accepted. 2016.
  2. Haiqin Yang, Zenglin Xu, Michael R. Lyu, and Irwin King. Budget Constrained Non-Monotonic Feature Selection. Neural Networks, 71:214-224, 2015.
  3. Haiqin Yang, Kaizhu Huang, Irwin King, and Michael R. Lyu. Maximum Margin Semi-supervised Learning with Irrelevant Data. Neural Networks, 70:90-102, 2015.
  4. Haiqin Yang, Guang Ling, Yuxin Su, Michael R. Lyu, and Irwin King. Boosting Response Aware Model-Based Collaborative Filtering. IEEE Transactions on Knowledge and Data Engineering, 27(8): 2064-2077, 2015.
  5. Haiqin Yang, Michael R. Lyu, and Irwin King. Efficient Online Learning for Multi-Task Feature Selection. ACM Transactions on Knowledge Discovery from Data, 7(2):1-27, August, 2013.
  6. Haiqin Yang, Zenglin Xu, Jieping Ye, Irwin King, and Michael R. Lyu. Efficient Sparse Generalized Multiple Kernel Learning. IEEE Transactions on Neural Networks, 22(3):433–446, March 2011.
  7. Haiqin Yang, Kaizhu Huang, Irwin King, and Michael R. Lyu. Localized Support Vector Regression for Time Series Prediction. Neurocomputing, 72(10-12):2659–2669, 2009.
  8. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu. Maxi-min Margin Machine: Learning Large Margin Classifiers Locally and Globally. IEEE Transactions on Neural Networks, 19(2):260–272, February 2008.  
  9. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu. Imbalanced Learning with Biased Minimax Probability Machine, IEEE Transactions on System, Man, and Cybernetics Part B, 36:913–923, 2006.
  10. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu. Maximizing Sensitivity in Medical Diagnosis Using Biased Minimax Probability Machine. IEEE Transactions on Biomedical Engineering, 53:821–831, 2006.
  11. Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, and Laiwan Chan. The Minimum Error Minimax Probability Machine. Journal of Machine Learning Research, 5:1253–1286, 2004.

 

Academic Book Chapters

  1. Haiqin Yang, Kaizhu Huang, Zenglin Xu, Irwin King, and Michael R. Lyu. Semi-supervised Learning with Mixed Unlabeled Data. In Machine Learning and Its Applications 2011, pages 221–242. 2011.
  2. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu. Local Learning vs. Global Learning: An Introduction to Maxi-Min Margin Machine. In Support Vector Machines: Theory and Applications, volume 177 of Studies in Fuzziness and Soft Computing, pages 113–132. Springer-Verlag, 2005.
  3.  Haiqin Yang, Irwin King, Laiwan Chan, and Kaizhu Huang. Financial Time Series Prediction Using Non-fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression. In Neural Information Processing: Research and Development, volume 152 of Studies in Fuzziness and Soft Computing, pages 334–350. Springer-Verlag, 2004.
  4. Kaizhu Huang, Irwin King, Michael R. Lyu, and Haiqin Yang. Improving Chow-Liu Tree Performance Based on Association Rules. In Neural Information Processing: Research and Development, volume 152 of Studies in Fuzziness and Soft Computing, pages 94–112. Springer-Verlag, 2004.

 

TopResearch Interests
  • Machine learning, big data analytics

TopTeaching Areas
  • Computer science, machine learning, data mining

TopMajor discipline(s)
  • Computer science, machine learning, data science