Dr. Haiqin Yang (楊海欽博士)

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Summary

Detailed can be found in Google Scholar or DBLP. Some toolboxes can be downloaded here.

Publications

Book

  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 Technology in China: Machine Learning. Zhejiang University Press with Springer Verlag, 2008.

Refereed Journal Articles

  1. Zhan Zhang, Ze Hu, Haiqin Yang, Rong Zhu, and Decheng Zuo. Factorization machines and deep views-based co-training for improving answer quality prediction in online health expert question-answering services. Journal of Biomedical Informatics 87: 21-36 (2018).

  2. Haiqin Yang and Lap Pong Cheung. Implicit Heterogeneous Features Embedding in Deep Knowledge Tracing. Cognitive Computation, 10(1): 3-14, 2018. IF: 3.441.

  3. Ze Hu, Zhan Zhang, Haiqin Yang, Qing Chen, Decheng Zuo. Predicting the Quality of Online Health Expert Question-Answering Services with Temporal Features in a Deep Learning Framework. Neurocomputing, 275: 2769-2782, 2018. IF: 3.211.

  4. Junjie Hu, Haiqin Yang, Michael R. Lyu, Irwin King, and Anthony Man-Cho So. Online Nonlinear AUC Maximization for Imbalanced Datasets. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 29(4): 882-895, 2018. IF: 6.108.

  5. Ze Hu, Zhan Zhang, Haiqin Yang, Qing Chen, Decheng Zuo. A Deep Learning Approach for Predicting the Quality of Online Health Expert Question-Answering Services. Journal of Biomedical Informatics 71: 241-253. 2017. IF: 2.753.

  6. Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks. ACM Transactions on Intelligent Systems and Technology (TIST), 8(1): 10:1-10:21, 2016. IF: 9.15.

  7. Haiqin Yang, Zenglin Xu, Michael R. Lyu, and Irwin King. Budget Constrained Non-Monotonic Feature Selection. Neural Networks, 71: 214-224, 2015. IF: 2.89.

  8. Haiqin Yang, Kaizhu Huang, Irwin King, and Michael R. Lyu. Maximum Margin Semi-supervised Learning with Irrelevant Data. Neural Networks, 70: 90-102, 2015. IF: 2.89.

  9. 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. IF: 2.476.

  10. Haiqin Yang, Michael R. Lyu, and Irwin King. Big Data Oriented Online Learning Algorithms. Communications of the CCF, 10(11): 36-40, November, 2014.

  11. 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. IF: 1.147.

  12. 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. IF: 2.633.

  13. 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. IF: 2.083.

  14. 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. IF: 2.769.

  15. 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. IF: 1.538.

  16. 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. IF: 2.302.

  17. 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. IF: 5.952.

Book Chapters

  1. Haiqin Yang, Linkai Luo, Lap Pong Chueng, David Ling, and Francis Chin. Deep Learning and its Applications to Natural Language Processing. In Deep Learning: Fundamentals, Theory, and Applications, 2019.

  2. 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: 221-242.

  3. 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: 113-132. Springer-Verlag, 2005.

  4. 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: 334-350. Springer-Verlag, 2004.

  5. 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: 94-112. Springer-Verlag, 2004.

Refereed Conferences

  1. Wenxiang Jiao, Haiqin Yang, Irwin King and Michael R. Lyu. HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition. NAACL-HLT 2019.

  2. Linkai Luo, Haiqin Yang, Sai Cheong Siu and Francis Y. L. Chin. Neural Machine Translation for Financial Listing Documents. ICONIP 2018.

  3. Linkai Luo, Haiqin Yang, and Francis Y. L. Chin. EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues. SocialNLP@ACL 2018. The second place in the shared task.

  4. Zhou Cheng, Chun Yuan, Jiancheng Li, and Haiqin Yang. TreeNet: Learning Sentence Representations with Unconstrained Tree Structure. IJCAI 2018: 4005-4011.

  5. Wenzhang Liu, Haiqin Yang, Yuewen Sun, and Changyin Sun. A Broad Neural Network Structure for Class Incremental Learning. ISNN 2018: 229-238.

  6. Lap Pong Cheung and Haiqin Yang. Heterogeneous Features Integration in Deep Knowledge Tracing. ICONIP (2) 2017: 653-662. Best student paper awards finalist.

  7. Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang. Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. AAAI 2017: 2796-2802.

  8. Yuxin Su, Haiqin Yang, Irwin King, and Michael R. Lyu. Distributed Information-Theoretic Metric Learning in Apache Spark. IJCNN 2016: 3306-3313.

  9. Xiaotian Yu, Haiqin Yang, Irwin King, and Michael R. Lyu. Online Non-negative Dictionary Learning via Moment Information for Sparse Poisson Coding. IJCNN 2016: 5094-5101.

  10. Shenglin Zhao, Tong Zhao, Haiqin Yang, Michael R. Lyu, Irwin King. STELLAR: Spatial-Temporal Latent Ranking for Successive Point-of-Interest Recommendation. AAAI 2016: 315-322. AR: 26%.

  11. Xixian Chen, Haiqin Yang, Irwin King, and Michael R. Lyu. Training-Efficient Feature Map for Shift-Invariant Kernels. IJCAI 2015: 3395-3401. AR: 28.8%.

  12. Junjie Hu, Haiqin Yang, Irwin King, Michael R. Lyu, and Anthony Man-Cho So. Kernelized Online Imbalanced Learning with Fixed Budgets. AAAI 2015: 2666-2672. AR: 11.95%.

  13. Jichuan Zeng, Haiqin Yang, Irwin King, and Michael R. Lyu. A Comparison of Lasso-type Algorithms on Distributed Parallel Machine Learning Platforms. In NIPS Workshop on Distributed Machine Learning and Matrix Computations, Montreal, Canada, 2014.

  14. Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Non-Monotonic Feature Selection for Regression. ICONIP (2) 2014: 44-51.

  15. Haiqin Yang, Junjie Hu, M.R. Lyu, and Irwin King. Online Imbalanced Learning with Kernels. In NIPS Workshop on Big Learning, Lake Tahoe, Nevada, USA, 2013.

  16. Chenxia Wu, Haiqin Yang, Jianke Zhu, Jiemi Zhang, Irwin King, and M.R. Lyu. Sparse Poisson Coding for High Dimensional Document Clustering. IEEE BigData 2013: 512-517. AR: 20%.

  17. Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. Where You Like to Go Next: Successive Point-of-Interest Recommendation. IJCAI 2013: 2605-2611. AR: 413/1473 (26%).

  18. Guang Ling, Haiqin Yang, Michael R. Lyu, and Irwin King. Response Aware Model-Based Collaborative Filtering. UAI 2012: 501-510. AR: 31%.

  19. Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. Fused Matrix Factorization with Geographical and Social Influence in Location-based Social Networks. AAAI 2012. AR: 26%.

  20. Guang Ling, Haiqin Yang, Irwin King, Michael R. Lyu. Online Learning for Collaborative Filtering. WCCI 2012: 1-8.

  21. Haiqin Yang, Shouyuan Chen, Michael R. Lyu, and Irwin King. Location-Based Topic Evolution. In MLBS’11: Proceedings of the 1st international workshop on Mobile location-based service: 89-98.

  22. Haiqin Yang, Shenghuo Zhu, Irwin King, and Michael R. Lyu. Can Irrelevant Data Help Semi-supervised Learning, Why and How? CIKM 2011: 937-946. AR: 15%.

  23. Haiqin Yang, Irwin King, and Michael R. Lyu. Online Learning for Multi-Task Feature Selection. CIKM 2010: 1693-1696. AR: 169/945 (17.9%).

  24. Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Online Learning for Group Lasso. ICML 2010: 1191-1198. AR: 152/594 (25.5%).

  25. Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, and Michael R. Lyu. Simple and Efficient Multiple Kernel Learning by Group Lasso. ICML 2010: 1175-1182. AR: 152/594 (25.5%).

  26. Haiqin Yang, Irwin King, and Michael R. Lyu. Multi-task Learning for One-class Classification. IJCNN 2010: 1-8.

  27. Haiqin Yang and Irwin King. Ensemble Learning for Imbalanced E-commerce Transaction Anomaly Classification. ICONIP 2009: 866-874.

  28. Haiqin Yang and Irwin King. Sprinkled Latent Semantic Indexing for Text Classification with background knowledge. ICONIP 2008: 53-60.

  29. Haiqin Yang, Kaizhu Huang, Irwin King, and Michael R. Lyu. Efficient Minimax Clustering Probability Machine by Generalized Probability Product Kernel. WCCI 2008: 4014-4019.

  30. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu. Local Support Vector Regression for Time Series Prediction. IJCNN 2006: 1622-1627.

  31. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu. Learning Large Margin Classifiers Locally and Globally. ICML 2004: 401-408.

  32. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu. Learning Classifiers from Imbalanced Data Based On Biased Minimax Probability Machine. CVPR(2) 2004: 558-563.

  33. Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, and Laiwan Chan. Biased Minimax Probability Machine for Medical Diagnosis. ISAIM 2004.

  34. Haiqin Yang, Kaizhu Huang, Laiwan Chan, Irwin King, and Michael R. Lyu. Outliers Treatment in Support Vector Regression for Financial Time Series Prediction. ICONIP 2004: 1260-1265.

  35. Haiqin Yang, Irwin King, and Laiwan Chan. Non-fixed and Asymmetrical Margin Approach to Stock Market Prediction Using Support Vector Regression. ICONIP 2002: 1398-1402.

  36. Haiqin Yang, Laiwan Chan, and Irwin King. Support Vector Machine Regression for Volatile Stock Market Prediction. IDEAL 2002: 391-396.