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 (preferred), slsun
{at} cs.ecnu.edu.cn |
Some news and information can also be found from my brief faculty profile in Chinese.
Biography
Shiliang
Sun received the B. E. degree from Beijing University of Aeronautics and
Astronautics (BUAA), and the M. E. and Ph.D. degrees in Pattern Recognition
and Intelligent Systems from 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 was a visiting researcher at the Department of Electrical Engineering, Columbia
University. He was a
program co-chair for ICONIP 2017 and is a member of the PASCAL (Pattern Analysis, Statistical Modelling and
Computational Learning) network of excellence.
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 and Structural Data Modeling.
Main Teaching
Pattern Recognition for undergraduate students;
Pattern Recognition and Machine Learning for graduate students.
Selected Recent Publications
(published or accepted) and Software
Y.
Liu, M. Yin, S. Sun. Multi-view learning and deep learning for
microscopic neuroblastoma pathology image diagnosis. Proceedings of the 15th Pacific Rim
International Conference on Artificial Intelligence (PRICAI), 2018.
J.
Chen, S. Sun, J. Zhao. Multi-label active learning with conditional
Bernoulli mixtures.
Proceedings of the 15th Pacific Rim International Conference on Artificial
Intelligence (PRICAI), 2018.
Y. Wu,
M. Lan, S. Sun, Q. Zhang, X. Huang. A learning error analysis for structured
prediction with approximate inference. NIPS, 2017: 6131-6141.
H.
Wang, J. Zhao, Z. Tang, S. Sun. Educational and
non-educational text classification based on deep Gaussian processes. Proceedings of the International Conference on
Neural Information Processing (ICONIP), 2017.
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.
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.
Last Update: Aug. 10, 2018