Full list of publications on both
Statistical Machine Learning and Mathematics (Harmonic Analysis), see my CV

2017

·
Y. Ying and D.X. Zhou. Unregularized
online learning algorithms with general loss functions. *Applied and
Computational Harmonic Analysis (ACHA), *42(2) 224-244, 2017.

·
and Online regularized
learning with pairwise loss functions. To appear in *Advances in Computational Mathematics*, 2017.

2016

·
Y. Ying, L. Wen and S. Lyu. Stochastic
online AUC maximization. *Advances in Neural Information Processing
Systems (**NIPS)*, 2016. (Oral
presentation) MATLAB Code Video
presentation

·
Y. Ying and D.X. Zhou. Online
Pairwise Learning Algorithms. *Neural Computation, *28: 743-777, 2016.

·
Q. Cao, Z. C. Guo and Y. Ying, Generalization
bounds for metric and similarity learning. *Machine Learning Journal*,
102(1) 115-132, 2016.

·
M. Boissier, S. Lyu, Y. Ying, and D.-X.
Zhou. Fast
convergence of online pairwise learning algorithms. *International Conference on Artificial Intelligence and Statistics
(AISTATS)*, 2016.

·
X. Wang, M.C. Chang, Y. Ying, and S. Lyu. Co-Regularized
PLSA for multi-modal learning. *The
Thirtieth AAAI Conference on Artificial Intelligence (AAAI)*, 2016.

2015

·
F. Guest, R. Everson, Y. Ying and D. Huang. Towards
automatic prediction of tumor growth from CT images using machine learning
algorithms - a feasibility study. *European Cancer Congress*, 2015.

·
Y. Lei and Y. Ying. Generalization
analysis for multi-modal metric learning. To appear in *Analysis and
Applications*, 2015. Online Ready.

·
M. Rogers, C. Campbell and Y. Ying. Probabilistic
inference of biological networks via data integration. *BioMed**
Research International*, Article ID: 707453, 2015.

2014

·
J. Bohne, Y. Ying,
S. Gentric and M. Pontil, Large
margin local metric learning . *European
Conference on Computer Vision (ECCV)*, 2014.

·
Z. C. Guo and Y. Ying, Guaranteed classification via regularised similarity learning. *Neural Computation ,*
Vol. 26(3), 2014.

2013

·
Q. Cao, Y. Ying and Peng Li, Similarity
metric learning for face recognition. *IEEE International Conference on
Computer Vision (ICCV)*, 2013.

·
Q. Cao, Z. C. Guo and Y. Ying, Generalization bounds for metric
and similarity learning. *arXiv**
preprint, version 2 (under revision for Machine Learning Journal) *, 2013.

2012

·
Y. Ying, Multi-task
coordinate gradient learning. *ICML workshop on "object, functional
and structured data: towards next generation kernel-based methods", *2012.

·
Q. Cao, Y. Ying and P. Li, Distance metric
learning revisited. *ECML-PKDD ,* 2012. (version 1, April 2012)

·
Y. Ying and P. Li, Distance metric learning with
eigenvalue optimization. *Journal of Machine Learning Research (Special
topics on kernel and metric learning) ,* 13:1-26, 2012.

See also the application of our method on face verification
data (LFW) .

2011

·
C. Campbell and Y. Ying, Learning
with Support Vector Machines. *Morgan & Claypool Publishers*,
2011.

·
Y. Ying and K. Huang and C. Campbell, Distance metric
learning with sparse regularization. *Technical Report, University of
Exeter*, September, 2010.

2010

·
Y. Ying, Q. Wu and C. Campbell, Learning the coordinate
gradients, to appear in *Advances in Computational Mathematics*, 2010.

·
Y. Ying and C. Campbell, Rademacher
chaos complexity for learning the kernel, *Neural Computation*, Vol.
22(11), 2010. (version 1, October 2008)

This second reversion is a substantial
extension of the COLT (2009) conference paper: Generalization bounds for
learning the kernel. In particular, we provided a self-contained proof for
bounding the Rademacher chaos complexity by metric
entropy integrals and also corrected the inaccurate claim on generalization
bounds derived from the covering number approach (appeared at the end of
Section 3 of the COLT conference version).

·
K. Huang, Y. Ying and C. Campbell, Generalized sparse
metric learning with relative comparisons, *Journal of Knowledge and
Information Systems (KAIS)*, 2010.

2009

·
K. Huang, Y. Ying and C. Campbell, GSML: A unified
framework for sparse metric learning, *IEEE International Conference on
Data Mining (ICDM), 2009. *

·
Y. Ying, K. Huang and C. Campbell, Sparse metric learning via
smooth optimization, *Advances in Neural Information Processing Systems
(NIPS), *2009.

·
Y. Ying, C. Campbell and M. Girolami, Analysis
of SVM with indefinite kernels, *Advances in Neural Information
Processing Systems (NIPS), *2009. spotlight slide

MATLAB code
(version 1)

·
Y. Ying, K. Huang and C. Campbell, Enhanced protein
fold recognition through a novel data integration approach, *BMC
Bioinformatics* (Open
access), (2009) 10:267.

See its short paper Information
theoretic kernel integration , *NIPS workshop on
Learning from multiple sources, 2009.* Presentation

·
Y. Ying and C. Campbell, Generalization bounds for
learning the kernel, *Proceedings of the 22nd Annual Conference on
Learning Theory (COLT)*, 2009.

2008

·
Y. Ying, C. Campbell, T. Damoulas,
and M. Girolami, Class prediction from disparate
biological data sources using a simple multi-class multi-kernel algorithm,
In *4th IAPR International Conference on Pattern Recognition in
Bioinformatics*, 2009. (Was preprint, 2008)

·
T. Damoulas, Y.
Ying, M. Girolami, and C. Campbell, Inferring sparse kernel
combination and relevance vectors: an application to subcelluar
localization of proteins , *International
Conference on Machine Learning and Applications (ICMLA)*, 2008.

·
Y. Ying and C. Campbell, Learning coordinate
gradients with multi-task kernels , *Proceedings
of the 21st Annual Conference on Learning Theory (COLT)*, 2008.

MATLAB code available under request.

·
P. Agius, Y. Ying
and C. Campbell, Bayesian
unsupervised learning with multiple data types , *Statistical
Applications in Genetics and Molecular Biology*, Vol. 8: Iss.
1, 2009. (Was Technical Report, January, 2008)

·
P. Li, Y.Ying and
C. Campbell, A variational approach to semi-supervised clustering, *Proceedings
of ESSAN ,*
2009. (Was preprint, 2008)

2007

·
Y. Ying, P. Li and C. Campbell, A marginalized variational Bayesian approach to the analysis of array data,
*BMC Proceedings of the Workshop on Machine Learning in Systems Biology*,
2007.

·
A. Argyriou, C. A.
Micchelli, M. Pontil, and
Y. Ying, A spectral regularization
framework for multi-task structure learning, *Advances in Neural
Information Processing Systems (NIPS)*, 2007.

·
A. Caponnetto, C.
A. Micchelli, M. Pontil,
and Y. Ying, Universal
multi-task kernels, *Journal of Machine Learning Research*, 9 (2008),
1615-1646. (Was Technical Report, University College London, December 2006)

Characterization of universal
matrixed-valued (and more general operator-valued) kernels. These characterizations
are highlighted with numerous examples of paractical
importance in multi-task learning.

2006

·
M. Pontil and Y.
Ying, Online gradient
descent learning algorithms, *Foundations of Computational Mathematics*,
5 (2008), 561-596. (Was Technical Report, University College London, 2005)

Generalization analysis of Online Stochastic
Gradient Descent algorithms in reproducing kernel Hilbert space. In particular,
we show that their error rates are competitive with offline regularization
algorithms.

·
Y. Ying and D.X. Zhou, Online regularized
classification algorithms, *IEEE Trans. Inform. Theory (regular paper)*,
11 (2006), 4775-4788.

2005

·
Y. Ying and D.X. Zhou, Learnability of
Gaussians with flexible variances , *Journal of
Machine Learning Research*, 8 (2007), 249-276. (Was Technical Report, City
University of Hong Kong, 2004)

Characterization of statistical consistency
for learning the kernel algorithms by the V-gamma dimension of the set of
candidate kernels.

·
Q. Wu, Y. Ying and D.X. Zhou, Multi-kernel regularized
classifiers, *Journal of Complexity*, 2006. (Was preprint, 2004)

·
Y. Ying, Convergence analysis of
online algorithms, *Advances in Computational Mathematics*, 27 (2007),
273-291. (Was preprint, City University of Hong Kong, 2005).

·
Q. Wu, Y. Ying and D.X. Zhou, Learning rates of
least-square regularized regression, *Foundations of Computational Mathematics*,
6 (2006), 171-192.

2004

·
Y. Ying, McDiarmid's
inequalities of Bernstein and Bennett forms, *Technical Report, City
University of Hong Kong*, 2004.

·
Q. Wu, Y. Ying and D.X. Zhou, Learning theory: from
regression to classification, *Topics in Multivariate Approximation and
Interpolation, K.Jetter et.al.,
Editors*, (2004), 101--134.

·
D.R. Chen, Q. Wu, Y. Ying and D.X. Zhou, Support vector machine soft
margin classifiers: error analysis, *Journal of Machine Learning Research*,
5 (2004).

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