徐增林

徐增林

徐增林,电子科技大学教授、博士生导师,中组部“青年千人计划”、“四川省千人计划”入选者。现任统计机器智能与学习实验室主任。徐增林教授主要研究兴趣为机器学习及其在社会网络分析、互联网、计算生物学、信息安全等方面的应用。他在包括NIPS, ICML, IJCAI, AAAI, IEEE TPAMI, IEEE TNNLS在内的著名会议和刊物发表论文80多篇,Google引用1500多次,发表专著1部,并于2012年在多伦多召开的国际人工智能大会(AAAI)和2017年11月在广州召开的国际神经信息处理会议(ICONIP)上做教学报告。徐增林教授是JMLR,IEEE TPAMI等机器学习与人工智能领域主要期刊的审稿人和香港教育资助局的基金评审人;多次担任人工智能领域的主要国际会议如AAAI/IJCAI等会议的程序委员会成员。他是2010年神经信息处理大会(NIPS)分会—社会计算中的机器学习研讨会,2013年和2014年IEEE 大数据大会分会—可扩展的机器学习研讨会的组织委员会主席。他是亚洲机器学习会议(ACML)2015年的Workshop Co-chair。徐增林教授是2015年人工智能领域顶级国际会议AAAI的最佳学生论文奖提名奖的指导老师之一,2016年亚洲机器学习会议(ACML)最佳学生论文奖亚军指导老师,2016年9月获得由亚太神经网络协会(APNNS)颁发的Young Researcher Award。

徐增林教授于2009年毕业于香港中文大学计算机科学与工程专业,师从香港中文大学工程学院副院长、亚太神经网络协会APPNS常务副会长Irwin King教授和ACM Fellow、IEEE Fellow、美国科学促进会AAAS Fellow Michael R. Lyu教授。他先后在美国密西根州立大学、德国马克思普朗克信息研究所及萨尔大学、美国普渡大学等著名研究机构访问和从事学术研究工作;主要合作者包括密西根州立大学Rong Jin教授、芬兰科学院院士/IEEE会士Erkki Oja教授、普渡大学Alan Qi教授、Ninghui Li教授等。

主要学术成果和学术贡献

a) 专著和编著

  1. Zenglin Xu and Irwin King. Introduction to Semi-supervised Learning. CRC Press, 2018 (expected).
  2. Yi Fang, Zenglin Xu, Jiang Bian, and Ziad Al Bawab. International Journal of Web Science, Special Issue on Social Web Search and Mining. Inderscience, 2013.
  3. Zenglin Xu, Irwin King, and Michael R. Lyu. More Than Semi-supervised Learning: A Unified View on Learning with Labeled and Unlabeled Data. LAP LAMBERT Academic Publishing, 2010.

b) 书籍章节

  1. Zenglin Xu, Mingzhen Mo, and Irwin King. Computational intelligence. In Alexandru Floares, editor, Semi-supervised Learning, pages 1-16. Nova Science Publishers, 2012.
  2. Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, and Zhangbin Zhou. A novel discriminative naive bayesian network for classification. In A. Mittal and A. Kassim, editors, Bayesian Network Technologies: Applications and Graphical Models, pages 1-12. IDEA Group Inc., New York, 2007.

c) 期刊文章

  1. Liyang Hao, Siqi Liang, Jinmian Ye and Zenglin Xu. TensorD: A tensor decomposition library in TensorFlow Neurocomputing, 318, 196-200, 2018.
  2. Zhao Kang, Liangjian Wen, Wenyu Chen and Zenglin Xu. Low-rank kernel learning for graph-based clustering Knowledge-Based Systems, 2018. DOI: doi.org/10.1016/j.knosys.2018.09.009.
  3. Zenglin Xu, Bin Liu, Shandian Zhe, Haoli Bai, Zihan Wang and Jennifer Neville. Variational Random Function Model for Network Modeling IEEE transactions on neural networks and learning systems, 99, 1-7, 2018.
  4. Shudong Huang, Peng Zhao, Yazhou Ren, Tianrui Li, and Zenglin Xu. Self-paced and soft-weighted nonnegative matrix factorization for data representation, Knowledge-based System, 2018. DOI:10.1016/j.knosys.2018.10.003
  5. Shudong Huang, Yazhou Ren, and Zenglin Xu. Robust Multi-View Data Clustering with Multi-View Capped-Norm K-means, Neurocomputing, 2018.
  6. Shudong Huang, Zhao Kang, and Zenglin Xu. Self-weighted Multi-View Clustering with Soft Capped Norm, Knowledge-Based Systems, 2018.
  7. Lirong He, Bin Liu, Guangxi Li, Yongpan Sheng, Yafang Wang, Zenglin Xu. Knowledge Base Completion by Variational Bayesian Neural Tensor Decomposition. Cognitive Computation, 2018
  8. Bin Liu, Yingming Li, Zenglin Xu, Manifold regularized matrix completion for multi-label learning with ADMM. Neural Networks, https://doi.org/10.1016/j.neunet.2018.01.011, 2018
  9. Shudong Huang, Zenglin Xu, Jiancheng Lv, Adaptive Local Structure Learning for Document Co-clustering, Knowledge-Based Systems, https://doi.org/10.1016/j.knosys.2018.02.020, 2018
  10. Shudong Huang, Hongjun Wang, Tao Li, Tianrui Li, and Zenglin Xu. Robust Graph Regularized Nonnegative Matrix Factorization for Clustering, Data Mining and Knowledge Discovery, 32(2): 483-503 (2018).
  11. Zenglin Xu, Shandian Zhe,Yuan(Alan) Qi and Peng Yu. Association Discovery and Diagnosis of Alzheimer’s Disease with Bayesian Multiview Learning. Journal of Artificial Intelligence Research, 56 (2016).
  12. Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Budget constrained non-monotonic feature selection. Neural Networks, 71, 214-224, 2015
  13. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Bayesian nonparametric models for multiway data analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.37, no.2, pp.475-487, 2015.
  14. 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, 2011.
  15. Zenglin Xu, Irwin King, Michael R. Lyu, and Rong Jin. Discriminative semi-supervised feature selection via manifold regularization. IEEE Transactions on Neural Networks, 21(7):1033-1047, 2010.
  16. Zenglin Xu, Kaizhu Huang, Jianke Zhu, Irwin King, and Michael R. Lyu. A novel kernel-based maximum a posteriori classification method. Neural Networks, 22(7):977-987, 2009.
  17. Zenglin Xu, Irwin King, and Michael R. Lyu. Feature selection based on minimum error minimax probability machine. International Journal of Pattern Recognition and Artificial Intelligence, 21(8):1-14, 2007.

d) 国际会议文章

  1. Yazhou Ren, Xiaohui Hu, Ke Shi, Guoxian Yu, Dezhong Yao, and Zenglin Xu. Semi-supervised DenPeak Clustering with Pairwise Constraints Pacific Rim International Conference on Artificial Intelligence, 2018
  2. Zhao Kang, Xiao Lu, Jinfeng Yi, Zenglin Xu: Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification, IJCAI, 2018.
  3. Hao Liu, Lirong He, Haoli Bai, Kun Bai, Zenglin Xu: Structured Inference for Recurrent Hidden Semi-Markov Model, IJCAI, 2018.
  4. Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu: Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition. CVPR, 2018.
  5. Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska: SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks, PPoPP, 2018.
  6. Zhao Kang, Chong Peng, Qiang Cheng, Zenglin Xu: Unified Spectral Clustering with Optimal Graph. AAAI, 2018
  7. Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang: Learning with Incomplete Labels. AAAI, 2018
  8. Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang: Learning with Feature Network and Label Network Simultaneously. AAAI 2018
  9. Xiaofan Que, Yazhou Ren, Jiayu Zhou, Zenglin Xu: Regularized Multi-source Matrix Factorization for Diagnosis of Alzheimer’s Disease. ICONIP(1) 2017: 463-473
  10. Yazhou Ren, Peng Zhao, Yongpan Sheng, Dezhong Yao, Zenglin Xu: Robust Softmax Regression for Multi-class Classification with Self-Paced Learning. IJCAI 2017: 2641-2647
  11. Guangxi Li, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, Michael R. Lyu: Simple and efficient parallelization for probabilistic temporal tensor factorization. IJCNN 2017: 1-8
  12. Shudong Huang, Zenglin Xu, Fei Wang: Nonnegative matrix factorization with adaptive neighbors. IJCNN 2017: 486-493
  13. Liqiang Wang, Yafang Wang, Bin Liu, Lirong He, Shijun Liu, Gerard de Melo, Zenglin Xu: Link prediction by exploiting network formation games in exchangeable graphs. IJCNN 2017: 619-626
  14. Yazhou Ren, Peng Zhao, Zenglin Xu, Dezhong Yao: Balanced self-paced learning with feature corruption. IJCNN 2017: 2064-2071
  15. Bin Liu, Zenglin Xu, Bo Dai, Haoli Bai, Xianghong Fang, Yazhou Ren, Shandian Zhe: Learning from semantically dependent multi-tasks. IJCNN 2017: 3498-3505
  16. Yiyang Zhao, Linnan Wang, Wei Wu, George Bosilca, Richard W. Vuduc, Jinmian Ye, Wenqi Tang,Zenglin Xu: Efficient Communications in Training Large Scale Neural Networks. ACM Multimedia (Thematic Workshops) 2017: 110-116
  17. Hao Liu, Haoli Bai, Lirong He, Zenglin Xu: Stochastic Sequential Neural Networks with Structured Inference. CoRR abs/1705.08695 (2017)
  18. Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang: Learning with Feature Network and Label Network Simultaneously. In AAAI’17: Proceedings of the 26th AAAI Conference on Artificial Intelligence. 1410-1416
  19. Yingming Li, Ming Yang, Zenglin Xu, and Zhongfei (Mark) Zhang Multi-view Learning with Limited and Noisy Tagging. In IJCAI’16: Proceedings of the 25th International Joint Conference on Artificial Intelligence.
  20. Shandian Zhe, Yuan Qi, Youngja Park, Zenglin Xu, Ian Molloy, and Suresh Chari DinTucker: Scaling up Gaussian Process Models on Large Multidimensional Arrays . In AAAI’16: Proceedings of the 26th AAAI Conference on Artificial Intelligence.
  21. Yingming Li, Ming Yang, Zenglin Xu, and Zhongfei (Mark) Zhang Learning with Marginalized Corrupted Features and Labels Together. In AAAI’16: Proceedings of the 26th AAAI Conference on Artificial Intelligence.
  22. Shandian Zhe, Zenglin Xu, Xinqi Chu, Yuan Qi and Yongja Park Scalable Nonparametric Multiway Data Analysis. In AISTATS’15: Proceedings of the 18th Proceedings of International Conference on Artificial Intelligence and Statistics. 2015. (AR: 127/442= 28.7%)
  23. Shandian Zhe, Zenglin Xu, and Yuan Qi. Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimer’s Disease. In AAAI’15: Proceedings of the 25th AAAI Conference on Artificial Intelligence. Outstanding student paper honorable mention, 2015. (AR: 531/1991= 26.7%)
  24. Zenglin Xu, Rong Jin, Bin Shen and Shenghuo Zhu. Nystrom Approximation for Sparse Kernel Methods: Theoretical Analysis and Empirical Evaluation In AAAI’15: Proceedings of the 25th AAAI Conference on Artificial Intelligence. 2015. (AR: 531/1991= 26.7%)
  25. Christopher Gates, Ninghui Li, Zenglin Xu, Suresh N. Chari, Ian Molloy, and Youngja Park. Detecting Insider Information Theft Using Features from File Access Logs. European Symposium on Research in Computer Security (ESORICS), 2014.
  26. Bin Shen, Zenglin Xu and Jan P. Allebach. Kernel Tapering: a Simple and Effective Approach to Sparse Kernels for Image Processing. International Conference on Image Processing, 2014.
  27. Shandian Zhe, Zenglin Xu and Yuan (Alan) Qi. Joint association discovery and diagnosis of Alzheimer’s disease by supervised heterogeneous multiview learning. Pacific Symposium on Biocomputing, 2014.
  28. Shouyuan Chen, Irwin King, Michael R. Lyu, and Zenglin Xu. Recovering pairwise interaction tensor. Neural Information Processing Systems (NIPS), 2013. (AR: 360/1420= 25.3%, Spotlight: 52/1420 = 3.7%)
  29. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Infinite tucker decomposition: Nonparametric bayesian models for multiway data analysis. In ICML’12: Proceedings of the 29th International Conference on Machine Learning, pages 1023-1030, New York, NY, USA, 2012. Omnipress. (AR: 243/890 = 27.3%)
  30. Feng Yan, Zenglin Xu, and Yuan (Alan) Qi. Sparse matrix-variate gaussian process blockmodels for network modeling. In UAI’11: Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence,
    pages 745-752. AUAI Press, 2011. (AR: 96/285=33.6%)
  31. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Sparse matrix-variate t process blockmodels. In AAAI ‘11: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. AAAI Press, 2011. (AR: 242/975=24.8%)
  32. Zenglin Xu, Rong Jin, Shenghuo Zhu, Michael R. Lyu, and Irwin King. Smooth optimization for effective multiple kernel learning. In AAAI ‘10: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. AAAI Press, 2010. (AR: 264/982=26.9%)
  33. Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, and Michael R. Lyu. Simple and efficient multiple kernel learning by group lasso. In ICML ‘10: Proceedings of the 27th International Conference on Machine Learning, pages 1175-1182. Omnipress, 2010. (AR: 152/594=25.6%)
  34. Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Online learning for group lasso. In ICML ‘10: Proceedings of the 27th International Conference on Machine Learning, pages 1191-1198. Omnipress, 2010. (AR: 152/594=25.6%)
  35. Kaizhu Huang, Rong Jin, Zenglin Xu, and Cheng-Lin Liu. Robust metric learning by smooth optimization. In UAI ‘10: Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, pages 244-251. AUAI Press, 2010. (AR: 88/260=33.8%)
  36. Zenglin Xu, Rong Jin, Michael R. Lyu, and Irwin King. Discriminative semi-supervised feature selection via manifold regularization. In IJCAI’09: Proceedings of the 21th International Joint Conference on Artificial Intelligence, pages 1303-1308, 2009.
  37. Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu, and Zhirong Yang. Adaptive regularization for transductive support vector machine. In Y. Bengio, L. Bottou, J. Lafferty, and C. Williams, editors, Advances in Neural Information Processing Systems 22 (NIPS), pages 2125-2133. 2009. (AR: 263/1105= 23.8%, Spotlight: 87/1105 = 7.8%)
  38. Zhirong Yang, Irwin King, Zenglin Xu, and Errki Oja. Heavy-tailed symmetric stochastic neighbor embedding. In Y. Bengio, L. Bottou, J. Lafferty, and C. Williams, editors, Advances in Neural Information Processing Systems 22 (NIPS), pages 2169-2177. 2009. (AR: 263/1105= 23.8%, Spotlight: 87/1105 = 7.8%)
  39. Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu, and Irwin King. Non-monotonic feature selection. In ICML ‘09: Proceedings of the 26th Annual International Conference on Machine Learning, pages 1145-1152, New York, NY, USA, 2009. ACM. (160/640 = 25%)
  40. Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, and Colin Campbell. Supervised self-taught learning: Actively transferring knowledge from unlabeled data. In IJCNN ‘09: International Joint Conference on Neural Networks, pages 1272-1277. IEEE, 2009.
  41. Zenglin Xu, Rong Jin, Irwin King, and Michael Lyu. An extended level method for efficient multiple kernel learning. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21 (NIPS), pages 1825-1832. 2008. (AR: 250/1022 = 24%)
  42. Zenglin Xu, Rong Jin, Kaizhu Huang, Irwin King, and Michael R. Lyu. Semi-supervised text categorization by active search. In CIKM ‘08: Proceedings of the thirteenth ACM international conference on Information and knowledge management, pages 1517-1518, New York, NY, USA, 2008. ACM Press. (AR: 256/772 = 33%)
  43. Kaizhu Huang, Zenglin Xu, Irwin King, and Michael R. Lyu. Semi-supervised learning from general unlabeled data. In ICDM ‘08: Proceedings of IEEE International Conference on Data Mining, pages 273-282, Los Alamitos, CA, USA, 2008. IEEE Computer Society. (AR: 70/724 = 9%)
  44. Jianke Zhu, Steven C. Hoi, Zenglin Xu, and Michael R. Lyu. An effective approach to 3d deformable surface tracking. In ECCV’08: Proceedings of the 10th European Conference on Computer Vision, pages 766-779, Berlin,
    Heidelberg, 2008. Springer-Verlag.
  45. Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, and Michael R. Lyu. Efficient convex relaxation for transductive support vector machine. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 1641-1648. MIT Press, Cambridge, MA, 2007. (217/975 = 22%)
  46. Zenglin Xu, Irwin King, and Michael R. Lyu. Web page classification with heterogeneous data fusion. In WWW ‘07: Proceedings of the 16th International Conference on World Wide Web, pages 1171-1172, New York, NY, USA, 2007. ACM Press.

科研项目

  1. 中央高校教师基础研究类项目,多源数据分析及在外科急诊病房的辅助决策中的应用,4万,2017/1-2018/12。
  2. 中央高校重大重点培育类项目,基于社会网络大数据的知识计算理论研究与应用,100万, 2017/1-2019/12。
  3. 电子科大985配套经费,先进机器学习平台关键技术研究,200万,2014-2018。
  4. 国家青千千人启动经费,先进机器学习平台关键技术研究,200万,2015-2017。
  5. 中国科学院网络数据科学与技术重点实验室开放基金,可扩展的贝叶斯学习算法及在大规模社会网络中的应用,主持,6万元,2015-2016。
  6. 中央高校基础科研经费,10万元,基于矩阵分布的贝叶斯学习算法及在社会网络分析中的应用研究,2015-2016。
  7. 国家自然科学基金科学部应急管理项目,基于矩阵分布的统计机器学习算法的专业运动员复杂社会网络构建及应用研究,2015/1-2015/12,20万元,主研。
  8. 国家自然科学基金项目,大规模张量分析中的非参贝叶斯学习技术研究,2016/1-2019/12,73万。