徐增林(哈爾濱工業大學（深圳）計算機科學與技術學院教授)

徐增林(哈爾濱工業大學（深圳）計算機科學與技術學院教授)

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徐增林：男，哈爾濱工業大學（深圳）計算機科學與技術學院教授、博士生導師，創建統計機器智慧型與學習實驗室。徐主要研究興趣為機器學習及其在社會網路分析、網際網路、計算生物學、信息安全等方面的套用。在包括IEEE TPAMI, IEEE TNN，NIPS, ICML, IJCAI, AAAI等重要會議和刊物發表論文50多篇，引用千餘次，發表專著2部，獲得2015年AAAI大會最佳學生論文獎提名、ACML2016最佳學生論文獎亞軍（Runner Up）。於2012年在多倫多召開的國際人工智慧大會(AAAI)上做教學報告。是JMLR， IEEE TPAMI等機器學習與人工智慧領域主要期刊的審稿人和國家自然基金委、科技部、香港教育資助局的基金評審人；多次擔任人工智慧領域的主要國際會議如AAAI/IJCAI等會議的程式委員會成員；多次擔任機器學習和大數據研究方面的研討會的組織委員會主席。

基本介紹

  • 中文名：徐增林
  • 職務：博士生導師
  • 性別：男
  • 職稱：教授
人物經歷,學習經歷,工作經歷,科研方向,研究項目,組織會議,論文列表,國際會議文章,

人物經歷

學習經歷

1998.09-2002.06 學士 西安工程大學 計算機科學與技術專業
2002.09-2005.06 碩士 西安交通大學 計算機軟體與理論專業
2005.09-2009.06 博士 香港中文大學 計算機科學與工程專業

工作經歷

2009.07-2010.07 副研究員 德國薩爾大學與馬克思普朗克信息所
2010.08-2014.05 副研究員 美國普渡大學
2014.05至今 教授 電子科技大學

科研方向

實驗室以機器學習技術及其套用為主要研究方向。研究方向包括：半監督學習、核學習、貝葉斯學習、特徵選擇與提取、多任務學習、多視角學習、主動學習、線上學習、矩陣分析、張量分析、深度學習、最佳化算法、可擴展學習等。主要套用領域包括網際網路、推薦系統、社會網路分析、生物信息學、神經信息學、健康數據分析、空間安全數據分析等 .

研究項目

1. 電子科大985配套經費，先進機器學習平台關鍵技術研究，主持，2014-2018
3. 國家自然科學基金面上項目，大規模貝葉斯張量分析技術研究，主持，2016-2019
4. 中國科學院網路數據科學與技術重點實驗室開放基金，可擴展的貝葉斯學習算法及在大規模社會網路中的套用，主持，2015-2016
5. 中央高校基礎科研經費，基於矩陣分布的貝葉斯學習算法及在社會網路分析中的套用研究，2015-2016
6. 國家自然科學基金科學部應急管理項目，基於矩陣分布的統計機器學習算法的專業運動員複雜社會網路構建及套用研究，2015/1-2015/12，主研。
7. 大規模張量分析中的非參貝葉斯學習技術研究 2016.01-2019.12 國家級 國家自然科學基金項目。

組織會議

1. 大數據算法與科學論壇於2014年11月30日全天在成都，電子科技大學（清水河新校區），圖書館168人報告廳。
台大Chih-Jen Lin教授做關於Large-scale linear classification: status and challenges的報告，時間2014年12月1日，地點：電子科技大學（清水河新校區），圖書館600人報告廳
2. The 2nd Workshop on Scalable Machine Learning: Theory and Applications, In conjunction with the 2014 IEEE International Conference on Big Data, Washington D.C., Oct, 2014.
3. The 1st Workshop on Scalable Machine Learning: Theory and Applications, In conjunction with IEEE Big Data Conference 2013, Santa Clara, CA, Oct, 2013.

論文列表

[1] Zenglin Xu, Zhe Shandian Qi Yuan and Yu Peng. Association discovery and diagnosis of Alzheimer's disease with Bayesian multiview learning, Journal of Artificial Intelligence Research, v56, p247-268, June 1, 2016.
[2] Liu Bin, Zenglin Xu*, Wu Shuang and Wang Fei. Manifold regularized matrix completion for multilabel classification, Pattern Recognition Letters, v80, p58-63, September 1, 2016.
[3] Zenglin Xu, Yan Feng and Qi Yuan. Bayesian nonparametric models for multiway data analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence8 (TPAMI), v37, n2, p475-487, February 1, 2015.
[4] 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. v4, p3115-3121, 2015.
[5] Yang Haiqin, Zenglin Xu, Lyu Michael R and King Irwin. Neural Networks, v71, p214-224, November 01, 2015.
[6] Chen Shouyuan, Lyu Michael R., King Irwin and Xu, Zenglin. Exact and stable recovery of pairwise interaction tensors, Advances in Neural Information Processing Systems 26, NIPS 2013.
[7] 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, v2, p1023-1030, 2012.
[8] 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, v1, p543-548, 2011.
[9] 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, v1, p637-642, 2010.
[10] Zenglin Xu, King Irwin, Lyu Michael Rung-Tsong and Jin Rong. Discriminative semi-supervised feature selection via manifold regularization, IEEE Transactions on Neural Networks, v21, n7, p1033-1047, July 2010.
[11] 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, p 1175-1182, 2010.
[12] 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, p1303-1308, 2009.
[13] Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu and Zhirong Yang. Adaptive regularization for transductive support vector machine, Advances in Neural Information Processing Systems 22 (NIPS), p 2125-2133, 2009.
[14] 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, v382, 2009.
[15] Zenglin Xu, Rong Jin, Irwin King and Michael Lyu. An extended level method for efficient multiple kernel learning, Advances in Neural Information Processing Systems 21(NIPS), p1825-1832, 2009.
[16] Zenglin Xu, Jin Rong, Zhu Jianke, King Irwin and Lyu Michael R. Efficient convex relaxation for transductive Support Vector Machine, Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference, 2009.
[17] Zenglin Xu, Huang Kaizhu, Zhu Jianke, King Irwin and Lyu Michael R. A novel kernel-based maximum a posteriori classification method, Neural Networks, v22, n7, p977-987, September 2009.
[18] 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, p1517-1518, 2008.
[19] Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, and Michael R. Lyu. Efficient convex relaxation for transductive support vector machine, Advances in Neural Information Processing Systems 20(NIPS), 2007.
[20] 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, p1171-1172, 2007.
[21] Zenglin Xu, King Irwin and Lyu Michael R. Feature selection based on minimum error minimax probability machine, International Journal of Pattern Recognition and Artificial Intelligence, v21, n8, p 1279-1292, December 2007.
[22] Zenglin Xu, Zhu Jianke, Lyu Michael R. and King Irwin. Maximum margin based semi-supervised spectral kernel learning, IEEE International Conference on Neural Networks-Conference Proceedings, p418-423, 2007.

國際會議文章

  • 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.
  • 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.
  • 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%)
  • Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. In nite tucker decomposition: Non-parametric 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%)
  • 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%)
  • 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 Arti cial Intelligence. AAAI Press, 2011. (AR: 242/975=24.8%)
  • 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%)
  • Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, and Michael R. Lyu. Simpleand 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%)
  • 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%)
  • 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%)
  • Zenglin Xu, Rong Jin, Michael R. Lyu, and Irwin King. Discriminative semisupervised feature selection via manifold regularization. In IJCAI '09: Proceedings of the 21th International Joint Conference on Arti cial Intelligence, pages 1303-1308, 2009.
  • 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%)
  • Zhirong Yang, Irwin King, Zenglin Xu, and Errki Oja. Heavy-tailed symmetric stochastic neighbor embedding. In Y. Bengio, L. Bottou, J. La erty,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%)
  • 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%)
  • Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, and Colin Campbell. Supervised self-taught learning: Actively transferring knowledge from unlabeleddata. In IJCNN '09: International Joint Conference on Neural Networks, pages 1272-1277. IEEE, 2009.
  • 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%)
  • 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%)
  • 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%)
  • 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.
  • 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%)
  • Zenglin Xu, Jianke Zhu, Irwin King, and Michael Lyu. Kernel maximum aposteriori classification with error bound analysis. In ICONIP '07: Proceedings of the International Conference on Neural Information Processing, pages 841-850,2007.
  • Zenglin Xu, Jianke Zhu, Michael R. Lyu, and Irwin King. Maximum margin based semi-supervised spectral kernel learning. In IJCNN '07: Proceedings of 20th International Joint Conference on Neural Network, pages 418-423, 2007.
  • Zenglin Xu, Irwin King, and Michael R. Lyu. Web page classification with heterogeneous data fusion.

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