Preprint

o
W. Shen, Z. Yang, Y. Ying and X. Yuan. Stability and generalization of
stochastic gradient descent for pairwise learning. Preprint, 10/2018.

o
Y. Feng and Y. Ying. Learning with
correntropy-induced losses for regression with mixture of symmetric stable
noise. ArXiv preprint, 4/2018.

2018

o
Q. Fang, M. Xu and Y. Ying. Faster
convergence of a randomized coordinate descent method for linearly constrained
optimization problems. *Analysis and Applications*, 16(5):
741-755, 2018.

o
M. Natole Jr, Y. Ying and S. Lyu. Stochastic
proximal algorithms for AUC maximization. *International Conference on
Machine Learning (ICML), 2018.*

o
S. Lyu and Y.
Ying. **A univariate bound of area under ROC**. *International Conference on
Uncertainty in Artificial Intelligence (UAI), Montery Bay, CA*, 2018

o
Y. Wei, M.-C.
Chang, Y. Ying, S. Lim, and S. Lyu.** Explain black- box image classifications using
superpixel-based interpretation**. *International Conference on
Pattern Recognition (ICPR)*, *Beijing, China*, 2018.

o
J Bohne Y.
Ying, S. Gentric, and M. Pontil. Learning
local metrics from pairwise similarity data, *Pattern Recognition*,
75: 315-326, 2018.

2017

o 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.

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

2016

o 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

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

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

o 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.

o 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

o 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.

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

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

2014

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

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

2013

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

o 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

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

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

o 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.

2011

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

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

2010

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

o 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).

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

2009

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

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

o 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)

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

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

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

2008

o 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)

o 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.

o 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.

o 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)

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

2007

o 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.

o 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.

o 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

o 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)

o 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.

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

2005

o 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.

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

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

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

2004

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

o 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.

o 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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