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 |
Updates and other information can also be found from my brief faculty profile in Chinese and my GitHub page.
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 is a member of the PASCAL (Pattern Analysis, Statistical Modelling and Computational
Learning) network of excellence and a program co-chair for ICONIP 2017.
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, Deep Learning;
---Computer
Vision, Natural Language Processing.
Main Teaching
Pattern Recognition for undergraduate students;
Pattern Recognition and Machine Learning for graduate students.
Book
Shiliang
Sun, Jing Zhao. Pattern Recognition and Machine Learning. Tsinghua University
Press, Beijing, 2020. [link]
Shiliang
Sun, Liang Mao, Ziang Dong, Lidan Wu. Multiview Machine Learning. Springer,
Singapore, 2019. [link]
Selected Recent Publications
(published or accepted) and Software
K.
Chen, S. Sun. Knowledge-based conversational recommender systems enhanced by
dialogue policy learning. International Joint Conference on Knowledge Graphs
(IJCKG), 2021. Dec. 6-8, Virtual Event, Thailand.
Y.
Liu, S. Sun. SagaNet: A small sample gated network for pediatric cancer
diagnosis. Proceedings of the 38th International Conference on Machine Learning
(ICML), PMLR 139: 6947-6956, 2021.
D.
Zong, J. Zhao, S. Sun. ASHF-Net: Adaptive sampling and hierarchical folding
network for robust point cloud completion. AAAI, 2021: 3625-3632.
S.
Sun, D. Zong. LCBM: A multi-view probabilistic model for multi-label
classification. IEEE Transactions on Pattern Analysis and Machine Intelligence,
2020.
S.
Sun, X. Xie, C. Dong. Multiview learning with generalized eigenvalue proximal
support vector machines. IEEE Transactions on Cybernetics, 2019, 49: 688-697.
S. Sun,
S. He. Generalizing expectation
propagation with mixtures of exponential family distributions and an
application to Bayesian logistic regression.
Neurocomputing, 2019.
S.
Sun, Y. Liu, L. Mao. Multi-view
learning for visual violence recognition with maximum entropy discrimination
and deep features. Information Fusion, 2019, 50:
43-53. [Data]
O.
Rivasplata, C. Szepesvari, J. Shawe-Taylor, E. Parrado-Hernandez, S. Sun.
PAC-Bayes bounds for stable algorithms with instance-dependent priors. NIPS,
2018.
P.
Huang, X. Xie, S. Sun. Multi-view
opinion mining with deep learning. Neural
Processing Letters, 2018.
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.
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: Sep. 25, 2021