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 |
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.
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.
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.
Current Teaching
Pattern
Recognition and Machine Learning: Fall, 2006, Graduate Students.
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.
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.
Last Update: July 2017