个人简历

科研项目
大规模非参贝叶斯张量分解
新一代认知神经网络
Books(专著)
Zenglin Xu and Irwin King. Introduction to Semi-supervised Learning. CRC Press, 2020 (expected).
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.
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.

研究领域

机器学习,量子机器学习,自然语言处理"致力于解决涉及现代大数据分析的关键建模和计算的挑战，实现复杂系统在自然语言处理，计算机视觉，社会计算，网络空间安全，生物信息学和生物医学应用等领域的研究。为此研究由各种应用驱动的稀疏、关系、动态、深度学习模型，并为这些模型开发精确、高效和可扩展的算法。
机器学习理论与方法
多源异构数据学习：多核学习 / 多视图学习 / 多任务学习 / 张量数据分析 / 迁移学习
贝叶斯深度学习
分布式学习与联邦学习
量子机器学习
机器学习应用
医疗大数据分析
网络数据分析
序列数据分析
知识图谱与自动问答"

近期论文

Shudong Huang, Zenglin Xu, Ivor W Tsang, Zhao Kang. Auto-weighted multi-view co-clustering with bipartitegraphs. Information Sciences, 512, 18-30, 2020.
Shudong Huang, Zenglin Xu, Zhao Kang, Yazhou Ren. Regularized Nonnegative Matrix Factorization withAdaptive Local Structure Learning. Neurocomputing, 2020.
Yazhou Ren, Shudong Huang, Peng Zhao, Minghao Han, Zenglin Xu. Self-paced and Auto-weighted Multi-viewClustering. Neurocomputing, 2020.
Liangjiang Wen, Xueyang Zhang, Haoli Bai, Zenglin Xu. Structured Pruning of Recurrent Neural Networksthrough Neuron Selection. Neural Networks, 2019.
Zhao Kang, Xinjia Zhao, Chong Peng, Hongyuan Zhu, Joey Tianyi Zhou, Xi Peng, Wenyu Chen, Zenglin Xu.Partition level multiview subspace clustering. Neural Networks, 2019.
Zhao Kang, Guoxin Shi, Shudong Huang, Wenyu Chen, Xiaorong Pu, Joey Tianyi Zhou, Zenglin Xu. Multi-graphfusion for multi-view spectral clustering. Knowledge-Based Systems, 105102, 2019.
Dan Ma, Bin Liu, Zhao Kang, Jiayu Zhou, Jianke Zhu, Zenglin Xu. Two birds with one stone: Transformingand generating facial images with iterative GAN. Neurocomputing, 2019.
Shudong Huang, Zhao Kang, Zenglin Xu. Auto-weighted Multi-view Clustering via Deep Matrix DecompositionPattern Recognition, 107015, 2019.
Yazhou Ren, Uday Kamath, Carlotta Domeniconi, Zenglin Xu. Parallel boosted clustering Neurocomputing,351, 87-100, 2019.
Yazhou Ren, Xiaofan Que, Dezhong Yao, Zenglin Xu. Self-paced multi-task clustering Neurocomputing, 350,212-220, 2019.
Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, Zenglin Xu. Semi-supervised deep embeddedclustering. Neurocomputing, 325, 121-130, 2019.
Shudong Huang, Zhao Kang, Ivor W Tsang, Zenglin Xu. Auto-weighted multi-view clustering via kernelizedgraph learning Pattern Recognition, 88, 174-184, 2019.
Zhao Kang, Haiqi Pan, Steven CH Hoi, Zenglin Xu. Robust Graph Learning from Noisy Data IEEE transactionson cybernetics, 2019.
Bin Liu, Lirong He, Yingming Li, Shandian Zhe, Zenglin Xu. NeuralCP: Bayesian Multiway Data Analysis withNeural Tensor Decomposition Cognitive Computation, 10(6), 1051-1061, 2018.
Liyang Hao, Siqi Liang, Jinmian Ye, and Zenglin Xu. TensorD: A tensor decomposition library in TensorFlowNeurocomputing, 318, 196-200, 2018.
Yazhou Ren, Kongrong Hu, Xinyi Dai, Lili Pan, Steven CH Hoi, and Zenglin Xu: Semi-supervised Deep Embed-ded Clustering Neurocomputing, 2018.
Zhao Kang, Liangjian Wen, Wenyu Chen and Zenglin Xu. Low-rank kernel learning for graph-based clusteringKnowledge-Based Systems, 2018.
Zenglin Xu, Bin Liu, Shandian Zhe, Haoli Bai, Zihan Wang and Jennifer Neville. Variational Random FunctionModel for Network Modeling IEEE transactions on neural networks and learning systems, 99, 1-7, 2018.
Shudong Huang, Peng Zhao, Yazhou Ren, Tianrui Li, and Zenglin Xu. Self-paced and soft-weighted nonnegativematrix factorization for data representation, Knowledge-based System, 2018.
Shudong Huang, Yazhou Ren, and Zenglin Xu. Robust Multi-View Data Clustering with Multi-View Capped-Norm K-means, Neurocomputing, 2018.
Shudong Huang, Zhao Kang, and Zenglin Xu. Self-weighted Multi-View Clustering with Soft Capped Norm,Knowledge-Based Systems, 2018.
Lirong He, Bin Liu, Guangxi Li, Yongpan Sheng, Yafang Wang, and Zenglin Xu. Knowledge Base Completionby Variational Bayesian Neural Tensor Decomposition. Cognitive Computation, 2018
Bin Liu, Yingming Li, and Zenglin Xu, Manifold regularized matrix completion for multi-label learning withADMM. Neural Networks, 2018.
Shudong Huang, Zenglin Xu, and Jiancheng Lv, Adaptive Local Structure Learning for Document Co-clustering,Knowledge-Based Systems, 2018
Shudong Huang, Hongjun Wang, Tao Li, Tianrui Li, and Zenglin Xu. Robust Graph Regularized NonnegativeMatrix Factorization for Clustering, Data Mining and Knowledge Discovery, 32(2): 483-503 (2018).
Zenglin Xu, Shandian Zhe,Yuan(Alan) Qi and Peng Yu. Association Discovery and Diagnosis of Alzheimer’sDisease with Bayesian Multiview Learning. Journal of Artificial Intelligence Research, 56 (2016).
Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Budget constrained non-monotonic feature selection. Neural Networks, 71, 214-224, 2015
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.
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.
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.
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.
Conference Papers (国际会议)

Junyu Lu, Chenbin Zhang, Zeying Xie, Guang Ling, Tom Chao Zhou, Zenglin Xu: Constructing InterpretiveSpatio-Temporal Features for Multi-Turn Responses Selection Proceedings of the 57th Annual Meeting of theAssociation for Computational Linguistics(ACL), 2019.
Yongpan Sheng, Zenglin Xu: Coherence and Salience-Based Multi-Document Relationship Mining Asia-PacificWeb (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and BigData, 2019.
Ao Liu, Lizhen Qu, Junyu Lu, Chenbin Zhang, Zenglin Xu: Machine Reading Comprehension: Matching andOrders Proceedings of the 28th ACM International Conference on Information and Knowledge Management,2019.
Zhao Kang, Zipeng Guo, Shudong Huang, Siying Wang, Wenyu Chen, Yuanzhang Su, Zenglin Xu: MultiplePartitions Aligned Clustering Proceedings of the 28th International Joint Conference on Artificial Intelligence(IJCAI), 2019.
Zhonghui You, Jinmian Ye, Kunming Li, Zenglin Xu, Ping Wang: Adversarial noise layer: Regularize neuralnetwork by adding noise IEEE International Conference on Image Processing (ICIP), 2019.
Shudong Huang, Wei Shi, Zenglin Xu, Ivor W Tsang: Iterative Orthogonal Federated Multi-view Learning IJCAIWorkshop on Federated Machine Learning, 2019.
Jian Liang, Yuren Cao, Chenbin Zhang, Shiyu Chang, Kun Bai, Zenglin Xu: Additive Adversarial Learning forUnbiased Authentication, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2019).
Maolin Wang, Chenbin Zhang, Yu Pan, Jing Xu and Zenglin Xu.: Tensor Ring Restricted Boltzmann Machines,(IJCNN, 2019).
Zhao Kang, Yiwei Lu, Yuanzhang Su, Changsheng Li, and Zenglin Xu: Similarity Learning via Kernel PreservingEmbedding, Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019). (AR: 1150/7095=16.2%)
Yu Pan, Jing Xu, Jinmian Ye, Maolin Wang, Fei Wang, Kun Bai, and Zenglin Xu: Compressing RecurrentNeural Networks with Tensor Ring Decomposition for Action Recognization. Proceedings of the 33rd AAAIConference on Artificial Intelligence (AAAI 2019). (AR: 1150/7095= 16.2%)
Shangchen Zhou, Shuai Yuan, Zhizhuo Zhang, and Zenglin Xu: Association Study of Alzheimer’s Disease withTree-Guided Sparse Canonical Correlation Analysis. International Conference on Neural Information Process-ing. 2018.
Haoli Bai, Zhuangbin Chen, Michael R. Lyu, Irwin King, and Zenglin Xu: Neural Relational Topic Models forScientific Article Analysis. CIKM 2018: 27-36
Yazhou Ren, Xiaohui Hu, Ke Shi, Guoxian Yu, Dezhong Yao, and Zenglin Xu. Semi-supervised DenPeakClustering with Pairwise Constraints. Pacific Rim International Conference on Artificial Intelligence, 2018
Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, and Zenglin Xu: Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition. CVPR, 2018.
Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, and Tim Kraska: Superneurons: dynamic GPU memory management for training deep neural networks. PPOPP 2018: 41-53
Hao Liu, Lirong He, Haoli Bai, Bo Dai, Kun Bai, Zenglin Xu: Structured Inference for Recurrent Hidden Semi-markov Model. IJCAI 2018: 2447-2453
Zhao Kang, Xiao Lu, Jinfeng Yi, Zenglin Xu: Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification, IJCAI 2018: 2312-2318.
Zhao Kang, Chong Peng, Qiang Cheng, Zenglin Xu: Unified Spectral Clustering With Optimal Graph. AAAI 2018: 3366-3373
Yingming Li, Zenglin Xu, Zhongfei Zhang: Learning With Incomplete Labels. AAAI 2018: 3588-3595
Xiaofan Que, Yazhou Ren, Jiayu Zhou, Zenglin Xu: Regularized Multi-source Matrix Factorization for Diagnosis of Alzheimer’s Disease. ICONIP (1) 2017: 463-473
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
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
Shudong Huang, Zenglin Xu, Fei Wang: Nonnegative matrix factorization with adaptive neighbors. IJCNN 2017: 486-493
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
Yazhou Ren, Peng Zhao, Zenglin Xu, Dezhong Yao: Balanced self-paced learning with feature corruption. IJCNN 2017: 2064-2071
Bin Liu, Zenglin Xu, Bo Dai, Haoli Bai, Xianghong Fang, Yazhou Ren, Shandian Zhe: Learning from semantically dependent multi-tasks. IJCNN 2017: 3498-3505
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
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
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.
Shandian Zhe, Yuan Qi, Youngja Park, Zenglin Xu, Ian Molloy, and Suresh Chari DinTucker: Scaling upGaussian Process Models on Large Multidimensional Arrays . In AAAI’16: Proceedings of the 26th AAAI Conference on Artificial Intelligence.
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.
Shandian Zhe, Zenglin Xu, Xinqi Chu, Yuan Qi and Yongja Park Scalable Nonparametric Multiway Data Anal-ysis. In AISTATS’15: Proceedings of the 18th Proceedings of International Conference on Artificial Intelligence and Statistics. 2015. (AR: 127/442= 28.7%)
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.
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.
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.
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.
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%)
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 Artificial 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 multiplekernel 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. 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%)
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 semi-supervised feature selection via manifold regularization. In IJCAI ’09: Proceedings of the 21th International Joint Conference on Artificial 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 inNeural 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. Lafferty, and C. Williams, editors, Advances in Neural Information Processing Systems22 (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 unlabeled data. In IJCNN ’09: International Joint Conference on NeuralNetworks, pages 1272–1277. IEEE, 2009.
Zenglin Xu, Rong Jin, Irwin King, and Michael Lyu. An extended level method for efficient multiple kernellearning. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural InformationProcessing 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 byactive search. In CIKM ’08: Proceedings of the thirteenth ACM international conference on Information andknowledge 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 unlabeleddata. 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 surfacetracking. 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 transductivesupport vector machine. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in NeuralInformation Processing Systems 20, pages 1641–1648. MIT Press, Cambridge, MA, 2007. (217/975 = 22%)
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.
期刊编委
Neurocomputing
Neural Networks
BMC Big Data Analytics
研讨会组织
2018: Advances in Unsupervised Deep Learning, in conjunction with PRICAI 2018, Nanjing, China？ 2014: Scalable Machine learning in conjunction with IEEE Big Data 2014, Washington, DC, US
2014: Scalable Machine learning in conjunction with IEEE Big Data 2014, Washington， DC, US
2013: Scalable Machine learning in conjunction with IEEE Big Data 2013, Santa Clara, CA, US
2010: Machine learning for social computing in conjunction with NIPS 2010, Vancouver, BC, Canada
期刊评审
IEEE Transaction on Pattern Recognition and Machine Intelligence, Journal of Machine Learning Research, IEEE Transaction on Neural Network, IEEE Transaction on KnowledgeDiscovery and Engineering, IEEE Transaction on Cybernetics, ACM TIST, ACM TKDD, NeuroComputing, Neural Computing and Applications,Pattern Recognition, Knowledge-based Systems
国际会议程序委员会：
2020: ICML, CVPR, ECCV, ACL, MIDL, ICLR, AAAI, IJCAI(Senior PC)
2019: CVPR, ICCV, ACL, IJCAI, CIKM, ICLR, AAAI(Senior PC)
2018: AAAI, IJCAI, ICLR, ICML, NIPS, ACML
2017: AAAI, IJCAI, NIPS, ICML
2016: AAAI, IJCAI, NIPS, ACML
2015: ICML, NIPS, AAAI, ACML
2014: NIPS, Web Intelligence
2013: NIPS, ACM CIKM, IJCAI, Web Intelligence, IJCNN, ICANN
2012: ICML, AAAI, AAAI AIWeb, ACM CIKM, ICPR, Web Intelligence
2011: NIPS, AAAI AIWeb, IJCAI, IJCNN, ICANN