Welcome to Shiliang Sun’s Home Page

Shiliang Sun, Professor

Head of the Pattern Recognition and Machine Learning Research Group

Dept. of Computer Science and Technology, East China Normal University

3663 North Zhongshan Road, Shanghai 200062, P. R. China

Email: shiliangsun {at} gmail.com, S.Sun {at} cs.ucl.ac.uk

硕士博士招生与专职科研人员(含博士后)聘用说明

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Recent News

March 2017: I am Program Co-Chair for ICONIP 2017. Call for papers is here. Submissions are welcome! See you in Guangzhou.

September 2016: We are organizing the IJCNN 2017 special session on Probabilistic Models and Kernel Methods. Here is the Call for Papers. Submissions are welcome!

April 2016: I give an invited talk on Tangent Space Intrinsic Manifold Regularization & Semi-supervised Support Vector Machines at the Workshop on Learning Methods and Applications of SVMs held by Shanxi University.

November 2015: I give an invited talk on Learning and Modeling for Gaussian Process Dynamical Models at the 1st International Conference on Data Science: Foundation and Applications (DSFA 2015), a satellite conference of the 8th International Congress on Industrial and Applied Mathematics (ICIAM 2015), and chair a session on Applications of Mathematical Foundation of Data Science.

November 2015: We are organizing the IJCNN 2016 special session on Probabilistic Models and Kernel Methods. Here is the call for papers. Submissions are welcome!

October 2015: I am in the TPC for the IJCNN 2016 special session on “Concept Drift, Domain Adaptation & Learning in Dynamic Environments”.

September 2015: I am the general co-chair for the International Workshop on Machine Learning for Big Data Analytics (MLBDA'15). Submissions are welcome!

February 2015: I am a PC member and reviewer for the IJCAI 2015 Machine Learning Track.

January 2015: I am in the Program Committee of the 2nd International Workshop on Advances in Learning from/with Multiple Learners that will be held at IJCNN 2015.

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Biography

Shiliang Sun received the B. E. degree in Automatic Control from the Department of Automatic Control, Beijing University of Aeronautics and Astronautics (BUAA), and the M. E. and Ph.D. degrees in Pattern Recognition and Intelligent Systems from the State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University. In 2004, he was awarded Microsoft Fellowship. In 2007, he joined the Department of Computer Science and Technology, East China Normal University (ECNU), and founded the Pattern Recognition and Machine Learning (PRML) Research Group. From 2009 to 2010, he was a visiting researcher at the Centre for Computational Statistics and Machine Learning (CSML) and the Department of Computer Science, University College London (UCL). From March to April 2012, he was a visiting researcher at the Department of Statistics and Biostatistics, Rutgers University. In July 2014, he is a visiting researcher at the Department of Electrical Engineering, Columbia University. He is a member of the PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) network of excellence. He serves on editorial boards of multiple international academic journals including IEEE Transactions on Neural Networks and Learning Systems, Information Fusion, Neurocomputing, and is an active reviewer for some journals including Journal of Machine Learning Research, Artificial Intelligence, IEEE Transactions on Signal Processing.

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Main Research Interests

---Probabilistic Models and Inference Techniques, Bayesian Nonparametric Models;

---Optimization Methods, Large-Scale Machine Learning;

---Statistical Learning Theory and Kernel Methods;

---Multiview Data Analysis, Sequential Data Modeling.

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Current Teaching

Pattern Recognition and Machine Learning: Fall, 2006, Graduate Students.

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Review Articles

J. Zhao, X. Xie, X. Xu, S. Sun. Multi-view learning overview: Recent progress and new challenges. Information Fusion, 2017.

S. Sun, C. Luo, J. Chen. A review of natural language processing techniques for opinion mining systems. Information Fusion, 2017.

S. Sun, J. Zhao, J. Zhu. A review of Nyström methods for large-scale machine learning. Information Fusion, 2015. [link]

S. Sun. A review of deterministic approximate inference techniques for Bayesian machine learning. Neural Computing and Applications, 2013. DOI: 10.1007/s00521-013-1445-4. [link]

S. Sun. A survey of multi-view machine learning. Neural Computing and Applications, 2013. DOI: 10.1007/s00521-013-1362-6. [link]

S. Sun, H. Shi, Y. Wu. A survey of multi-source domain adaptation. Information Fusion, 2015. http://dx.doi.org/10.1016/j.inffus.2014.12.003.

J. Shawe-Taylor, S. Sun. Kernel methods and support vector machines. Book Chapter for E-Reference Signal Processing, Elsevier, 2013. DOI: 10.1016/B978-0-12-396502-8.00026-7.

J. Shawe-Taylor, S. Sun. A review of optimization methodologies in support vector machines. Neurocomputing, 2011, 74 (17): 3609-3618.

S. Sun, J. Zhou. A review of adaptive feature extraction and classification methods for EEG-based brain-computer interfaces. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2014. 1746-1753.

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Selected Recent Publications (published or accepted) and Software

S. Sun, J. Paisley, Q. Liu. Location dependent Dirichlet processes. Proceedings of the International Conference on Intelligence Science and Big Data Engineering (IScIDE), 2017.

Q. Liu, S. Sun. Sparse multimodal Gaussian processes. Proceedings of the International Conference on Intelligence Science and Big Data Engineering (IScIDE), 2017.

C. Luo, S. Sun, J. Zhao. Variational hidden conditional random fields with beta processes. Proceedings of the 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2017.

C. Luo, S. Sun. Variational mixtures of Gaussian processes for classification. Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017. [Code]

H. Liu, L. Liu, T. D. Le, I. Lee, S. Sun, J. Li. Non-parametric sparse matrix decomposition for cross-view dimensionality reduction. IEEE Transactions on Multimedia, 2017. [Code]

J. Zhao, X. Xie, X. Xu, S. Sun. Multi-view learning overview: Recent progress and new challenges. Information Fusion, 2017.

Q. Liu, S. Sun. Multi-view regularized Gaussian processes. The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2017. [Code]

X. Xie, S. Sun. PAC-Bayes bounds for twin support vector machines. Neurocomputing, 2017.

S. Sun, C. Luo, J. Chen. A review of natural language processing techniques for opinion mining systems. Information Fusion, 2017.

S. Sun, J. Shawe-Taylor, L. Mao. PAC-Bayes analysis of multi-view learning. Information Fusion, 2016.

M. Yin, J. Zhao, S. Sun. Key course selection for academic early warning based on Gaussian processes. The 17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), 2016.

M. Yin, X. Xie, S. Sun. Key course selection in academic warning with sparse regression. The Chinese Conference on Pattern Recognition (CCPR), 2016.

L. Mao, S. Sun. Soft margin consistency based scalable multi-view maximum entropy discrimination. Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), 2016. [Code]

G. Chao, S. Sun. Consensus and complementarity based maximum entropy discrimination for multi-view classification. Information Sciences, 2016. [Code]

J. Zhao, S. Sun. Variational dependent multi-output Gaussian process dynamical systems. Journal of Machine Learning Research, 2016. [Code]

孙仕亮,陈俊宇. 大数据分析的硬件与系统支持综述. 小型微型计算机系统,2016年中国数据挖掘会议优秀稿件.

孙仕亮. 计算教育学与十大研究主题 (Computational education science and ten research directions). 中国人工智能学会通讯 (Communications of the Chinese Association for Artificial Intelligence), 2015, 5 (9): 15-16.

J. Zhao, S. Sun. High-order Gaussian process dynamical models for traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 2016. [Code]

Y. Zhou, S. Sun. Manifold partition discriminant analysis. IEEE Transactions on Cybernetics, 2016. [Code]

S. Sun, X. Xie, M. Yang. Multi-view uncorrelated discriminant analysis. IEEE Transactions on Cybernetics, 2015. [Code]

S. Sun, X. Xie. Semi-supervised support vector machines with tangent space intrinsic manifold regularization. IEEE Transactions on Neural Networks and Learning Systems, 2015. [Code]

G. Chao, S. Sun. Alternative multi-view maximum entropy discrimination. IEEE Transactions on Neural Networks and Learning Systems, 2015. [Code]

Y. Wu, S. Sun. An online learning algorithm for bilinear models. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2015.

J. Zhao, S. Sun. Revisiting Gaussian process dynamical models. Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. [Code]

S. Sun, J. Zhao, Q. Gao. Modeling and recognizing human trajectories with beta process hidden Markov models. Pattern Recognition, 2015.

S. Sun, J. Zhao, J. Zhu. A review of Nyström methods for large-scale machine learning. Information Fusion, 2015. [link]

S. Sun, H. Shi, Y. Wu. A survey of multi-source domain adaptation. Information Fusion, 2015. http://dx.doi.org/10.1016/j.inffus.2014.12.003.

J. Zhou, S. Sun. Gaussian process versus margin sampling active learning. Neurocomputing, 2015. [Code]

Y. Zhou, S. Sun. Local tangent space discriminant analysis. Neural Processing Letters, 2015.

G. Chao, S. Sun. Multi-kernel maximum entropy discrimination for multi-view learning. Intelligent Data Analysis, 2016.

Q. Wang, Y. Lu, S. Sun. Text detection in nature scene images using two-stage nontext filtering. Proceedings of the 13th International Conference on Document Analysis and Recognition (ICDAR), 2015.

H. Shi, S. Sun. Sparse uncorrelated cross-domain feature extraction for signal classification in brain-computer interfaces. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2015.

H. Shi, J. Xu, S. Sun. Uncorrelated transferable feature extraction for signal classification in brain-computer interfaces. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2015.

Y. Zhou, S. Sun. Semi-supervised tangent space discriminant analysis. Mathematical Problems in Engineering Special Issue on Machine Learning with Applications to Autonomous Systems, 2015, Article ID 706180. [Code]

X. Xie, S. Sun. Multitask centroid twin support vector machines. Neurocomputing, 2015. [Code]

J. Zhao, S. Sun. Variational dependent multi-output Gaussian process dynamical systems. Proceedings of the International Conference on Discovery Science (DS), 2014.

J. Zhou, S. Sun. Active learning of Gaussian processes with manifold-preserving graph reduction. Neural Computing and Applications, 2014.

X. Xie, S. Sun. Multi-view Laplacian twin support vector machines. Applied Intelligence, 2014.

J. Zhu, S. Sun. Multi-task sparse Gaussian processes with improved multi-task sparsity regularization. Proceedings of the 6th Chinese Conference on Pattern Recognition (CCPR), 2014.

J. Xu, L. Ding, S. Sun. Supervised Bayesian sparse coding for classification. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2014. 319-326.

S. Sun, J. Zhou. A review of adaptive feature extraction and classification methods for EEG-based brain-computer interfaces. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2014. 1746-1753.

M. Yang, S. Sun. Multi-view uncorrelated linear discriminant analysis for handwritten digit recognition. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2014. 4175-4181.

J. Zhu, S. Sun. Sparse Gaussian processes with manifold-preserving graph reduction. Neurocomputing, 2014, 138: 99-105.

X. Xie, S. Sun. Multi-view twin support vector machines. Intelligent Data Analysis, 2014. [Code]

S. Sun. A review of deterministic approximate inference techniques for Bayesian machine learning. Neural Computing and Applications, 2013. DOI: 10.1007/s00521-013-1445-4. [link]

Q. Gao, S. Sun. Trajectory-based human activity recognition with hierarchical Dirichlet process hidden Markov models. Proceedings of the 1st IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), 2013. 456-460.

S. Sun. Tangent space intrinsic manifold regularization for data representation. Proceedings of the 1st IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), 2013. 179-183. [slides] [Code]

S. Sun. Infinite mixtures of multivariate Gaussian processes. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), 2013. 1011-1016.

J. Zhu, Shiliang Sun. Single-task and multitask sparse Gaussian processes. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), 2013. 1033-1038. [best student paper nomination]

S. Sun, H. Shi. Bayesian multi-source domain adaptation. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), 2013. 24-28.

X. Xie, S. Sun. Multi-view clustering ensembles. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), 2013. 51-56.

S. Sun, G. Chao. Multi-view maximum entropy discrimination. Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013. 1706-1712. [Code]

S. Sun. A survey of multi-view machine learning. Neural Computing and Applications, 2013. DOI: 10.1007/s00521-013-1362-6. [link]

S. Sun, Z. Xu, M. Yang. Transfer learning with part-based ensembles. Lecture Notes in Computer Science, 2013, 7872: 271-282.

Y. Ji, S. Sun. Multitask multiclass support vector machines: Model and experiments. Pattern Recognition, 2013.

J. Shawe-Taylor, S. Sun. Kernel methods and support vector machines. Book Chapter for E-Reference Signal Processing, Elsevier, 2013. DOI: 10.1016/B978-0-12-396502-8.00026-7.

E. Parrado-Hernandez, A. Ambroladze, J. Shawe-Taylor, S. Sun. PAC-Bayes bounds with data dependent priors. Journal of Machine Learning Research, 2012.

R. Huang, S. Sun. Kernel regression with sparse metric learning. Journal of Intelligent and Fuzzy Systems, 2013.

S. Sun, Z. Hussain, J. Shawe-Taylor. Manifold-preserving graph reduction for sparse semi-supervised learning. Neurocomputing, 2013. DOI: 10.1016/j.neucom.2012.08.070.

S. Sun, R. Huang, Y. Gao. Network-scale traffic modeling and forecasting with graphical lasso and neural networks. Journal of Transportation Engineering, 2012.

W. Tu, S. Sun. Semi-supervised feature extraction for EEG classification. Pattern Analysis and Applications, 2013.

W. Tu, S. Sun. A subject transfer framework for EEG classification. Neurocomputing, 2012.

W. Tu, S. Sun. Cross-domain representation-learning framework with combination of class-separate and domain-merge objectives. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining, 2012.

Y. Ji, S. Sun, Y. Lu. Multitask multiclass privileged information support vector machines. Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012.

W. Tu, S. Sun. Dynamical ensemble learning with model friendly classifiers for domain adaptation. Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012.

Q. Gao, S. Sun. Trajectory-based human activity recognition using hidden conditional random fields. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), 2012.

R. Huang, S. Sun. Sequential training of semi-supervised classification based on sparse Gaussian process regression. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), 2012.

G. Chao, S. Sun. Applying a multitask feature sparsity method for the classification of semantic relations between nominals. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), 2012.

S. Sun, X. Xu. Variational inference for infinite mixtures of Gaussian processes with applications to traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 2011, 12 (2): 466-475. [Code]

J. Shawe-Taylor, S. Sun. A review of optimization methodologies in support vector machines. Neurocomputing, 2011, 74 (17): 3609-3618.

S. Sun, F. Jin. Robust co-training. International Journal of Pattern Recognition and Artificial Intelligence, 2011, 25 (7): 1113-1126.

S. Sun, Q. Chen. Hierarchical distance metric learning for large margin nearest neighbor classification. International Journal of Pattern Recognition and Artificial Intelligence, 2011, 25(7): 1073-1087.

S. Sun, Q. Zhang. Multiple-view multiple-learner semi-supervised learning. Neural Processing Letters, 2011, 34 (3): 229-240.

S. Sun, Y. Lu, Y. Chen. The stochastic approximation method for adaptive Bayesian classifiers: Towards online brain-computer interfaces. Neural Computing and Applications, 2011, 20 (1): 31-40.

S. Sun. Multi-view Laplacian support vector machines. Lecture Notes in Computer Science, 2011, 7121: 209-222. [Code]

W. Tu, S. Sun. Transferable discriminative dimensionality reduction. Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2011. 865-868.

Z. Xu, S. Sun. Multi-view transfer learning with adaboost. Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2011. 399-402.

S. Sun, J. Shawe-Taylor. Sparse semi-supervised learning using conjugate functions. Journal of Machine Learning Research, 2010, 11: 2423-2455.

S. Sun. Local within-class accuracies for weighting individual outputs in multiple classifier systems. Pattern Recognition Letters, 2010, 31 (2): 119-124.

Q. Zhang, S. Sun. Multiple-view multiple-learner active learning. Pattern Recognition, 2010, 43 (9): 3113-3119.

S. Sun, D. Hardoon. Active learning with extremely sparse labeled examples. Neurocomputing, 2010, 73: 2980-2988.

S. Sun. Extreme energy difference for feature extraction of EEG signals. Expert Systems with Applications, 2010, 37 (6): 4350-4357.

J. Shawe-Taylor, S. Sun. Discussion of ‘stability selection’, by Nicolai Meinshausen and Peter Bühlmann. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2010, 72(4): 451-453.

Z. Xu, S. Sun. An algorithm on multi-view adaboost. Lecture Notes in Computer Science, 2010, 6443: 355-362.

J. Li, S. Sun. Nonlinear combination of multiple kernels for support vector machines. Proceedings of the 20th International Conference on Pattern Recognition (ICPR), 2010. 2889-2892.

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Last Update: July 2017