Support Vector Machine Soft Margin Classifiers: Error Analysis. - Di-Rong Chen; Qiang Wu 003; Yiming Ying; Ding-Xuan Zhou (J. Mach. Learn. Res., 2004)

SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming. - Qiang Wu 003; Ding-Xuan Zhou (Neural Comput., 2005)

Learning Rates of Least-Square Regularized Regression. - Qiang Wu 003; Yiming Ying; Ding-Xuan Zhou (Found. Comput. Math., 2006)

Estimation of Gradients and Coordinate Covariation in Classification. - Sayan Mukherjee 001; Qiang Wu 003 (J. Mach. Learn. Res., 2006)

Multi-kernel regularized classifiers. - Qiang Wu 003; Yiming Ying; Ding-Xuan Zhou (J. Complex., 2007)

Characterizing the Function Space for Bayesian Kernel Models. - Natesh S. Pillai; Qiang Wu 003; Feng Liang; Sayan Mukherjee 001; Robert L. Wolpert (J. Mach. Learn. Res., 2007)

Localized Sliced Inverse Regression. - Qiang Wu 003; Sayan Mukherjee 001; Feng Liang (NIPS, 2008)

Learning with sample dependent hypothesis spaces. - Qiang Wu 003; Ding-Xuan Zhou (Comput. Math. Appl., 2008)

Application of integral operator for regularized least-square regression. - Hongwei Sun; Qiang Wu 003 (Math. Comput. Model., 2009)

Regularized least square regression with dependent samples. - Hongwei Sun; Qiang Wu 003 (Adv. Comput. Math., 2010)

Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence. - Qiang Wu 003; Justin Guinney; Mauro Maggioni; Sayan Mukherjee 001 (J. Mach. Learn. Res., 2010)

Estimating variable structure and dependence in multitask learning via gradients. - Justin Guinney; Qiang Wu 003; Sayan Mukherjee 001 (Mach. Learn., 2011)

Sparse PCA by iterative elimination algorithm. - Yang Wang 020; Qiang Wu 003 (Adv. Comput. Math., 2012)

Learning the coordinate gradients. - Yiming Ying; Qiang Wu 003; Colin Campbell (Adv. Comput. Math., 2012)

Empirical Mode Decomposition Analysis for Visual Stylometry. - James Michael Hughes; Dong Mao; Daniel N. Rockmore; Yang Wang 020; Qiang Wu 003 (IEEE Trans. Pattern Anal. Mach. Intell., 2012)

Regularization networks with indefinite kernels. - Qiang Wu 003 (J. Approx. Theory, 2013)

Learning theory approach to minimum error entropy criterion. - Ting Hu 002; Jun Fan; Qiang Wu 003; Ding-Xuan Zhou (J. Mach. Learn. Res., 2013)

Multiple authors Detection: a Quantitative Analysis of Dream of the Red Chamber. - Xianfeng Hu; Yang Wang 020; Qiang Wu 003 (Adv. Data Sci. Adapt. Anal., 2014)

Consistency Analysis of an Empirical Minimum Error Entropy Algorithm. - Jun Fan; Ting Hu 002; Qiang Wu 003; Ding-Xuan Zhou (CoRR, 2014)

A New Approach for Physiological Time Series. - Dong Mao; Yang Wang 020; Qiang Wu 003 (Adv. Data Sci. Adapt. Anal., 2015)

Sparse Representation in Kernel Machines. - Hongwei Sun; Qiang Wu 003 (IEEE Trans. Neural Networks Learn. Syst., 2015)

Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression. - Ting Hu 002; Qiang Wu 003; Ding-Xuan Zhou (IEEE Trans. Signal Process., 2016)

Bias corrected regularization kernel network and its applications. - Qiang Wu 003 (IJCNN, 2017)

Learning performance of regularized moving least square regression. - Hongzhi Tong; Qiang Wu 003 (J. Comput. Appl. Math., 2017)

Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network. - Zheng-Chu Guo; Lei Shi 010; Qiang Wu 003 (J. Mach. Learn. Res., 2017)

Overlapping Sliced Inverse Regression for Dimension Reduction. - Ning Zhang 013; Zhou Yu; Qiang Wu 003 (CoRR, 2018)

Online learning for supervised dimension reduction. - Ning Zhang 013; Qiang Wu 003 (Math. Found. Comput., 2019)

Machine learning-based microarray analyses indicate low-expression genes might collectively influence PAH disease. - Song Cui; Qiang Wu 003; James West; Jiangping Bai (PLoS Comput. Biol., 2019)

Distributed Minimum Error Entropy Algorithms. - Xin Guo 003; Ting Hu 002; Qiang Wu 003 (J. Mach. Learn. Res., 2020)

Modeling interactive components by coordinate kernel polynomial models. - Xin Guo 003; Lexin Li; Qiang Wu 003 (Math. Found. Comput., 2020)

Averaging versus voting: A comparative study of strategies for distributed classification. - Donglin Wang; Honglan Xu; Qiang Wu 003 (Math. Found. Comput., 2020)

Pairwise Learning for Imbalanced Data Classification. - Shu Liu; Qiang Wu 003 (CSCI, 2021)

On the Selection of Hyperparameters in Convolutional Neural Networks. - Donglin Wang; Qiang Wu 003 (CSCI, 2021)

Kernel gradient descent algorithm for information theoretic learning. - Ting Hu 002; Qiang Wu 003; Ding-Xuan Zhou (J. Approx. Theory, 2021)

Robust pairwise learning with Huber loss. - Shouyou Huang; Qiang Wu 003 (J. Complex., 2021)

Optimal Rates of Distributed Regression with Imperfect Kernels. - Hongwei Sun; Qiang Wu 003 (J. Mach. Learn. Res., 2021)

A Framework of Learning Through Empirical Gain Maximization. - Yunlong Feng; Qiang Wu 003 (Neural Comput., 2021)

Optimality of regularized least squares ranking with imperfect kernels. - Fangchao He; Yu Zeng; Lie Zheng; Qiang Wu 003 (Inf. Sci., 2022)

A statistical learning assessment of Huber regression. - Yunlong Feng; Qiang Wu 003 (J. Approx. Theory, 2022)

Fast Rates of Gaussian Empirical Gain Maximization With Heavy-Tailed Noise. - Shouyou Huang; Yunlong Feng; Qiang Wu 003 (IEEE Trans. Neural Networks Learn. Syst., 2022)

Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning. - Donglin Wang; Don Hong; Qiang Wu 003 (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)