[1] Michael R. Lyu ,  Irwin King ,  Jianke Zhu ,  Rong Jin ,  and  Zenglin Xu ,  Efficient convex relaxation for transductive support vector machinepp. 1641-1648, Aug 2007.

[2] Michael R. Lyu ,  Zenglin Xu ,  Steven C. Hoi ,  and  Jianke Zhu ,  An effective approach to 3d deformable surface tracking, In ECCV ’08: Proceedings of the 10th European Conference on Computer Vision, pp. 766-779, Aug 2008.

[3] Michael R. Lyu ,  Irwin King ,  Zenglin Xu ,  and  Kaizhu Huang ,  Semi-supervised learning from general unlabeled data, In ICDM ’08: Proceedings of IEEE International Conference on Data Mining, pp. 273-282,

[4] Michael RLyu ,  Irwin King ,  Kaizhu Huang ,  Rong Jin ,  and  Zenglin Xu ,  Semi-supervised text categorization by active search, In CIKM ’08: Proceedings of the thirteenth ACM international conference on Information and knowledge, pp. 1517-1518, Aug 2008.

[5] Michael Lyu ,  Irwin King ,  Rong Jin ,  and  Zenglin Xu ,  An extended level method for e cient multiple kernel learningpp. 1825-1832, Aug 2008.

[6] Irwin King ,  Michael R. Lyu ,  Jieping Ye ,  Rong Jin ,  and  Zenglin Xu ,  Non-monotonic feature selection, In ICML ’09: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1145-1152, Aug 2009.

[7] Errki Oja ,  Zenglin Xu ,  Irwin King ,  and  Zhirong Yang ,  Zhirong Yang, Irwin King, Zenglin Xu, and Errki Ojapp. 2169-2177,

[8] Zhirong Yang ,  Michael Lyu ,  Irwin King ,  Jianke Zhu ,  Rong Jin ,  and  Zenglin Xu ,  Adaptive regularization for transductive support vector machinepp. 2125-2133, Aug 2009.

[9] Irwin King ,  Michael R. Lyu ,  Rong Jin ,  and  Zenglin Xu ,  Discriminative semi-supervised feature selection via manifold regularizationpp. 1303, Aug 2009.

[10] Cheng-Lin Liu ,  Zenglin Xu ,  Rong Jin ,  and  Kaizhu Huang ,  Robust metric learning by smooth optimization, In UAI ’10: Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, pp. 244-251, Aug 2010.

[11] Michael R. Lyu ,  Irwin King ,  Zenglin Xu ,  and  Haiqin Yang ,  Online learning for group lassopp. 1191-1198, Aug 2010.

[12] Michael R. Lyu ,  Irwin King ,  Haiqin Yang ,  Rong Jin ,  and  Zenglin Xu ,  Simple and e cient multiple kernel learning by group lassopp. 1175{, Aug 2010.

[13] Irwin King ,  Michael R. Lyu ,  Shenghuo Zhu ,  Rong Jin ,  and  Zenglin Xu ,  Smooth optimization for effective multiple kernel learning, AAAI Press, Aug 2010.

[14] Yuan (Alan) Qi ,  Feng Yan ,  and  Zenglin Xu ,  Sparse matrix-variate t process blockmodels, AAAI Press, Aug 2011.

[15] Yuan (Alan) Qi ,  Zenglin Xu ,  and  Feng Yan ,  Sparse matrix-variate Gaussian process blockmodels for network modeling, In UAI ’11: Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, pp. 745–752, Aug 2011.

[16] Yuan (Alan) Qi ,  Feng Yan ,  and  Zenglin Xu ,  In nite tucker decomposition: Non-parametric Bayesian models for multiway data analysispp. 1023,

[17] Zenglin Xu ,  Michael R. Lyu ,  Irwin King ,  and  Shouyuan Chen ,  Recovering pairwise interaction tensorAug 2013.

[18] Yuan (Alan) Qi ,  Zenglin Xu ,  and  Shandian Zhe ,  Joint association discovery and diagnosis of Alzheimer’s disease by supervised heterogeneous multiview learning

[19] Jan P. Allebach ,  Zenglin Xu ,  and  Bin Shen ,  Kernel Tapering: a Simple and E effective Approach to Sparse Kernels for Image ProcessingAug 2014.

[20] Youngja Park ,  Ian Molloy ,  Suresh N. Chari ,  Zenglin Xu ,  Ninghui Li ,  and  Christopher Gates ,  Detecting Insider Information Theft Using Features from File Access LogsAug 2014.