Gradient Learning Approach for Variable Selection in Credit Scoring. - Quan-Wu Xiao; Lei Shi 010 (BIFE, 2009)

Learning theory viewpoint of approximation by positive linear operators. - Shaogao Lv; Lei Shi 010 (Comput. Math. Appl., 2010)

Hermite learning with gradient data. - Lei Shi 010; Xin Guo 003; Ding-Xuan Zhou (J. Comput. Appl. Math., 2010)

Classification with non-i.i.d. sampling. - Zheng-Chu Guo; Lei Shi 010 (Math. Comput. Model., 2011)

Normal estimation on manifolds by gradient learning. - Lei Shi 010; Ding-Xuan Zhou (Numer. Linear Algebra Appl., 2011)

Learning with coefficient-based regularization and ℓ1 -penalty. - Zheng-Chu Guo; Lei Shi 010 (Adv. Comput. Math., 2013)

Quantile regression with ℓ 1 - regularization and Gaussian kernels. - Lei Shi 010; Xiaolin Huang; Zheng Tian; Johan A. K. Suykens (Adv. Comput. Math., 2014)

Asymmetric v-tube support vector regression. - Xiaolin Huang; Lei Shi 010; Kristiaan Pelckmans; Johan A. K. Suykens (Comput. Stat. Data Anal., 2014)

Asymmetric least squares support vector machine classifiers. - Xiaolin Huang; Lei Shi 010; Johan A. K. Suykens (Comput. Stat. Data Anal., 2014)

Ramp loss linear programming support vector machine. - Xiaolin Huang; Lei Shi 010; Johan A. K. Suykens (J. Mach. Learn. Res., 2014)

Support Vector Machine Classifier With Pinball Loss. - Xiaolin Huang; Lei Shi 010; Johan A. K. Suykens (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Pinball Loss Minimization for One-bit Compressive Sensing. - Xiaolin Huang; Lei Shi 010; Ming Yan 006; Johan A. K. Suykens (CoRR, 2015)

Sequential minimal optimization for SVM with pinball loss. - Xiaolin Huang; Lei Shi 010; Johan A. K. Suykens (Neurocomputing, 2015)

Learning with the maximum correntropy criterion induced losses for regression. - Yunlong Feng; Xiaolin Huang; Lei Shi 010; Yuning Yang; Johan A. K. Suykens (J. Mach. Learn. Res., 2015)

Two-level ℓ1 minimization for compressed sensing. - Xiaolin Huang; Yipeng Liu 001; Lei Shi 010; Sabine Van Huffel; Johan A. K. Suykens (Signal Process., 2015)

Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction. - Xiaolin Huang; Yan Xia 002; Lei Shi 010; Yixing Huang; Ming Yan 006; Joachim Hornegger; Andreas K. Maier (CoRR, 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)

Convergence of Unregularized Online Learning Algorithms. - Yunwen Lei; Lei Shi 010; Zheng-Chu Guo (J. Mach. Learn. Res., 2017)

Solution Path for Pin-SVM Classifiers With Positive and Negative τ Values. - Xiaolin Huang; Lei Shi 010; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2017)

Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions. - Fanghui Liu 001; Lei Shi 010; Xiaolin Huang; Jie Yang 002; Johan A. K. Suykens (CoRR, 2018)

Pinball loss minimization for one-bit compressive sensing: Convex models and algorithms. - Xiaolin Huang; Lei Shi 010; Ming Yan 006; Johan A. K. Suykens (Neurocomputing, 2018)

Fast and strong convergence of online learning algorithms. - Zheng-Chu Guo; Lei Shi 010 (Adv. Comput. Math., 2019)

Sparse Kernel Regression with Coefficient-based $ ell_q-$regularization. - Lei Shi 010; Xiaolin Huang; Yunlong Feng; Johan A. K. Suykens (J. Mach. Learn. Res., 2019)

Robust mixed one-bit compressive sensing. - Xiaolin Huang; Haiyan Yang; Yixing Huang; Lei Shi 010; Fan He; Andreas K. Maier; Ming Yan 006 (Signal Process., 2019)

Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces. - Fanghui Liu 001; Lei Shi 010; Xiaolin Huang; Jie Yang 002; Johan A. K. Suykens (CoRR, 2020)

Fast algorithms for robust principal component analysis with an upper bound on the rank. - Ningyu Sha; Lei Shi 010; Ming Yan 006 (CoRR, 2020)

Preface of the special issue on analysis in data science: Methods and applications. - Xin Guo 003; Lei Shi 010 (Math. Found. Comput., 2020)

Realizing Data Features by Deep Nets. - Zheng-Chu Guo; Lei Shi 010; Shao-Bo Lin (IEEE Trans. Neural Networks Learn. Syst., 2020)

A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels. - Fanghui Liu 001; Xiaolin Huang; Lei Shi 010; Jie Yang 002; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2020)

Provably Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphs. - Zhuoqing Song; Lei Shi 010; Shi Pu 004; Ming Yan 006 (CoRR, 2021)

Generalization Properties of hyper-RKHS and its Applications. - Fanghui Liu 001; Lei Shi 010; Xiaolin Huang; Jie Yang 002; Johan A. K. Suykens (J. Mach. Learn. Res., 2021)

Analysis of regularized least-squares in reproducing kernel Kreĭn spaces. - Fanghui Liu 001; Lei Shi 010; Xiaolin Huang; Jie Yang 002; Johan A. K. Suykens (Mach. Learn., 2021)

Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate. - Zhuoqing Song; Weijian Li; Kexin Jin; Lei Shi 010; Ming Yan 006; Wotao Yin; Kun Yuan 001 (NeurIPS, 2022)

Capacity dependent analysis for functional online learning algorithms. - Xin Guo 003; Zheng-Chu Guo; Lei Shi 010 (CoRR, 2022)

Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks. - Zhuoqing Song; Lei Shi 010; Shi Pu 004; Ming Yan 006 (IEEE Trans. Signal Process., 2022)

Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks. - Guanhang Lei; Zhen Lei; Lei Shi 010; Chenyu Zeng; Ding-Xuan Zhou (CoRR, 2023)

Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels. - Fan He; Mingzhen He; Lei Shi 010; Xiaolin Huang; Johan A. K. Suykens (CoRR, 2023)

Learning With Asymmetric Kernels: Least Squares and Feature Interpretation. - Mingzhen He; Fan He; Lei Shi 010; Xiaolin Huang; Johan A. K. Suykens (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Solving Parametric PDEs with Radial Basis Functions and Deep Neural Networks. - Guanhang Lei; Zhen Lei; Lei Shi 010; Chenyu Zeng (CoRR, 2024)

Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning. - Fan He; Mingzhen He; Lei Shi 010; Xiaolin Huang; Johan A. K. Suykens (CoRR, 2024)

Long-time Integration of Nonlinear Wave Equations with Neural Operators. - Guanhang Lei; Zhen Lei; Lei Shi 010 (CoRR, 2024)

Classification with Deep Neural Networks and Logistic Loss. - Zihan Zhang; Lei Shi 010; Ding-Xuan Zhou (J. Mach. Learn. Res., 2024)

Coefficient-based regularized distribution regression. - Yuan Mao; Lei Shi 010; Zheng-Chu Guo (J. Approx. Theory, 2024)

Provably Accelerated Decentralized Gradient Methods Over Unbalanced Directed Graphs. - Zhuoqing Song; Lei Shi 010; Shi Pu 004; Ming Yan 006 (SIAM J. Optim., 2024)

Global Search and Analysis for the Nonconvex Two-Level ℓ₁ Penalty. - Fan He; Mingzhen He; Lei Shi 010; Xiaolin Huang (IEEE Trans. Neural Networks Learn. Syst., 2024)

Pairwise ranking with Gaussian kernel. - Guanhang Lei; Lei Shi 010 (Adv. Comput. Math., 2024)

Optimal gradient tracking for decentralized optimization. - Zhuoqing Song; Lei Shi 010; Shi Pu 004; Ming Yan 006 (Math. Program., 2024)

Optimality of Robust Online Learning. - Zheng-Chu Guo; Andreas Christmann; Lei Shi 010 (Found. Comput. Math., 2024)

A Decentralized Framework for Kernel PCA With Projection Consensus Constraints. - Fan He; Ruikai Yang; Lei Shi 010; Xiaolin Huang (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Optimal Convergence Rates of Deep Neural Network Classifiers. - Zihan Zhang; Lei Shi 010; Ding-Xuan Zhou (CoRR, 2025)