Yunlong Feng and Qiang Wu.
Tikhonov Regularization for Gaussian Empirical Gain Maximization in RKHS is Consistent.
Preprint 2021.
2024
Chendi Wang, Xin Guo, and Qiang Wu.
Learning with centered reproducing kernels.
Analysis and Applications,
2024, in press.
2023
Shu Liu* and Qiang Wu. Robust representation in deep learning.
DBKDA 2023: The Fifteenth International Conference on Advances
in Databases, Knowledge, and Data Applications,
page 27-32, 2023. (*PhD Student)
[proceeding link]
Donglin Wang*, Don Hong, and Qiang Wu.
Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning.
IEEE/ACM Transactions on Computational Biology and Bioinformatics,
20(2): 1581-1586, 2023. (*PhD student).
[journal link]
Donglin Wang*, Don Hong, and Qiang Wu.
Prediction of Loan Rate for Mortgage Data: Deep Learning versus Robust Regression.
Computational Economics,
61, pages 1137-1150 (2023). (*PhD student)
[journal link]
2022
Donglin Wang*, Don Hong and Qiang Wu.
Extracting Default Mode Network Based on Graph Neural Network for Resting State fMRI Study.
Frontiers in Neuroimaging,
1:963125, 2022. (*PhD Student)
[journal link]
Shouyou Huang, Yunlong Feng, and Qiang Wu.
Fast Rates of Gaussian Empirical Gain Maximization with Heavy-Tailed Noise.
IEEE Transactions on Neural Networks and Learning Systems,
33:10 (2022), 6038-6043.
[journal link]
Fangchao He, Yu Zeng, Lie Zheng, Qiang Wu.
Optimality of regularized least squares ranking with imperfect kernels.
Information Sciences, 589 (2022), 564-579.
[journal link]
Shouyou Huang, Yunlong Feng and Qiang Wu.
Learning theory of minimum error entropy under weak moment conditions.
Analysis and Applications,
Vol. 20, No. 01, pp. 121-139 (2022).
[journal link]
Yunlong Feng and Qiang Wu. A statistical learning assessment of Huber regression.
Journal of Approximation Theory,
Volume 273, January 2022, 105660. [arxiv]
[journal link]
2021
Donglin Wang* and Qiang Wu.
On the Selection of Hyperparameters in Convolutional Neural Networks.
Proceedings of CSCI 2021. (*PhD student)
[proceeding link]
Shu Liu* and Qiang Wu.
Pairwise Learning for Imbalanced Data Classification.
Proceedings of CSCI 2021. (*PhD student)
[proceeding link]
Xiaoqing Zheng, Hongwei Sun, and Qiang Wu.
Regularized least square kernel regression for streaming data.
Communications in Mathematical Sciences,
(2021), 19(6):1533-1548.
[Journal Link]
Hongwei Sun and Qiang Wu. Optimal rates of distributed regression with imperfect kernels.
JMLR, 22(171):1-34, 2021.
[arxiv]
[journal link]
Yunlong Feng and Qiang Wu. A Framework of Learning Through Empirical Gain Maximization.
Neural Computation, (2021) 33(6):1656-1697.
[arxiv]
[Journal Link]
Shouyou Huang and Qiang Wu.
Robust Pairwise Learning with Huber Loss.
Journal of Complexity, (2021) 66:101570.
[journal link]
Xia Cui, Song Cui, Dorothy Menefee, Qiang Wu, Nithya Rajan, Shyam Nair, Nate Phillips, Forbes Walker.
Predicting Carbon and Water Vapor Fluxes using Machine Learning and Novel Feature Ranking Algorithms.
Science of The Total Environment,
2021, 145130. [journal link]
Ting Hu, Qiang Wu and Ding-Xuan Zhou,
Kernel gradient descent algorithm for information theoretic learning,
Journal of Approximation Theory,
263 (2021), Article ID 105518.
[journal link]
2020
Xin Guo, Ting Hu and Qiang Wu,
Distributed Minimum Error Entropy Algorithms
JMLR,
21(126):1-31, 2020.
[journal link]
Yunlong Feng and Qiang Wu,
Learning under (1+ε)-moment conditions,
Applied
and Computational Harmonic Analysis,
49 (2020) 495-520.
[journal link]
Ting Hu, Qiang Wu and Ding-Xuan Zhou,
Distributed kernel gradient descent algorithm for
minimum error entropy principle,
Applied
and Computational Harmonic Analysis,
49(1): 229-256, 2020.
[journal link]
Hongzhi Tong and Qiang Wu,
Moving quantile regression,
Journal of Statistical Planning and Inference,
205 (2020), 46-63.
[journal link]
Xin Guo, Lexin Li and Qiang Wu,
Modeling Interactive Components by Coordinate Kernel Polynomial Models.
Mathematical Foundations of Computing.
2020.
[journal link]
Donglin Wang*, Honglan Xu**, and Qiang Wu.
Averaging versus voting: A comparative study of strategies for distributed classification.
Mathematical Foundations of Computing,
2020. (*PhD student; **Master Student)
[journal link]
2019
Cen Li, Michael Hains, John Wallin, and Qiang Wu.
Applying Data Science Methods for Early Prediction of Undergraduate Student Retention,
CSCI 2019, pp. 1337-1340.
[paper link]
Song Cui, Qiang Wu, James West, and Jiangping Bai,
Machine Learning-based Microarray Analyses Indicate Low-expression Genes Might Collectively Influence PAH Disease,
PLOS Computational Biology,
15:8 (2019), e1007264.
[journal link]
Ning Zhang*, Zhou Yu and Qiang Wu.
Overlapping sliced inverse regression for dimension reduction.
Analysis and Applications.
17:5 (2019), 715-736. (*PhD Student)
[journal link]
Ning Zhang* and Qiang Wu,
Online learning for supervised dimension reduction.
Mathematical Foundations of Computing,
2:2 (2019), 95-106. (*PhD student)
[journal link]
Fangchao He and Qiang Wu,
Bias corrected regularization kernel method in ranking,
Analysis and Applications,
17:1 (2019), 1-17.
[journal link]
2017
Zhengchu Guo, Lei Shi and Qiang Wu,
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network,
Journal of Machine Learning Research,
18(118):1-25, 2017.
[arxiv]
[journal link]
Qiang Wu,
Bias corrected regularization kernel network and its applications,
IJCNN 2017, pp. 1072-1079.
[Article Link]
Hongzhi Tong and Qiang Wu,
Learning performance of regularized moving least square regression,
Journal of Computational and Applied Mathematics,
325 (2017), 42-55.
[journal link]
2016
Xin Yang*, Qiang Wu, Don Hong and Jiancheng Zou,
Spatial regularization for neural network and
application in alzheimer's disease classification,
Future Technologies Conference 2016. (*PhD student)
C. A. Micchelli, M. Pontil, Qiang Wu, and D. X. Zhou,
Error bounds for learning the kernel,
Analysis and Applications,
14:6 (2016), 849-868.
[journal link]
Ting Hu, Qiang Wu and Ding-Xuan Zhou,
Convergence of gradient descent for minimum error entropy principle in linear regression,
IEEE Transactions on Signal Processing,
64:24 (2016), 6571-6579.
[journal link]
Jun Fan, Ting Hu, Qiang Wu, and Ding-Xuan Zhou,
Consistency analysis of minimum error entropy algorithm,
Applied
and Computational Harmonic Analysis, 41 (2016), pp. 164-189.
(Available online since 12/23/2014. Final version published on 5/18/2016.)
[arxiv]
[journal link]
Xiangfeng Hu, Yang Wang, and Qiang Wu,
Stylometry and Mathematical Study of Authorship. In A. Aldroubi et al. (eds.),
New Trends in Applied Harmonic Analysis,
pp 281-300,
Springer International Publishing, 2016.
[link]
Xin Yang*, Qiang Wu, J. Zou, D. Hong,
Spatial Regularization for Multitask Learning and
Application in fMRI Data Analysis,
British Journal of Mathematics & Computer Science,
14(4), 2016. Article no. BJMCS.23829 (*PhD student)
[journal link]
2015
H. Sun and Qiang Wu, Sparse Representation in Kernel Machines,
IEEE Transactions on Neural Networks
and Learning Systems, 26:10, (2015), 2576-2582.
[journal link]
D. Mao, Qiang Wu, and Y. Wang,
A New Approach for Physiological Time Series,
Advances in Adaptive Data Analysis,
Vol. 7, No. 1 (2015) 1550001 (13 pages)
[arxiv]
[journal link].
Ting Hu, Jun Fan, Qiang Wu, and Ding-Xuan Zhou,
Regularization schemes for minimum error entropy principle,
Analysis and Applications,
13:4 (2015), 437-455.
[journal link]
2014
X. Hu, Qiang Wu, and Y. Wang,
Multiple authors detection: a quantitative analysis of Dream of the Red Chamber,
Advances in Adaptive Data Analysis, Vol 6, No. 4, (2014) Article ID 1450012 (18 pages).
[arxiv]
[journal link].
2013
H. Sun and Qiang Wu, Indefinite Kernel Network with Dependent Sampling,
Analysis and Applications 11, 1350020 (2013) [15 pages].
[pdf]
[journal link]
Qiang Wu, F. Liang and S. Mukherjee,
Kernel sliced inverse regression: regularization and consistency,
Abstract and Applied Analysis , 2013.
[journal link]
Ting Hu, Jun Fan, Qiang Wu, and Ding-Xuan Zhou,
Learning Theory Approach to Minimum Error Entropy Criterion,
Journal of Machine Learning
Research, 14 (2013) 377-397.
[arxiv]
[journal link]
Qiang Wu, Regularization Networks with Indefinite Kernels,
Journal of Approximation Theory, 166 (2013),
1-18.
[journal
link]
2012
J. M. Hughes, D. Mao, D. N. Rockmore, Y. Wang and Qiang Wu,
Empirical mode decomposition analysis for visual stylometry, IEEE Transactions on
Pattern
Analysis and Machine Intelligence, 34:11 (2012), 2147-2157.
[pdf]
[journal
link]
Y. Ying, Qiang. Wu and C. Campbell, Learning the coordinate
gradients, Advances in Computational Mathematics,
37:3, (2012), 355-378. [journal
link]
Y. Wang and Qiang Wu,
Sparse PCA by iterative
elimination algorithm, Advances
in Computational Mathematics, 36 (2012),
137–151. [pdf]
[journal
link]
2011
H. W. Sun and Qiang Wu,
Least square regression with indefinite kernels and coefficient
regularization, Applied
and Computational Harmonica
Analysis, 30:1 (2011), 96--109.
[journal
link]
Qiang Wu, F. Liang and S. Mukherjee, Localized sliced
inverse regression, Journal
of Computational and Graphical Statistics, 19:4
(2010), 843-860. [journal
link] (This is a longer version of the paper published in 2008 NIPS proceedings.)
H.W. Sun and
Qiang Wu, Application of integral operator for regularized least-square
regression. Math.
Comput. Modelling, 49 (2009), no. 1-2, 276--285. [pdf] [journal
link]
Qiang Wu, Y. M. Ying and D. X. Zhou, Learning rates of
least square
regularized regression, Found. Comput. Math., 6:2
(2006), 171--192.
Qiang Wu, Y. M. Ying and D. X. Zhou, Learning theory: from
regression to classification, in "Topics in Multivariate
Approximation and
interpolation", pp. 257--290, K. Jetter et al editors,
Amsterdam;
Boston: Elsevier, 2006.
Qiang Wu and D. X. Zhou, Analysis of support vector machine
classification, J.
Comput. Anal. Appl., 8:2 (2006), 99--119.