Approximation capability of interpolation neural networks. - Feilong Cao; Shaobo Lin; Zongben Xu (Neurocomputing, 2010)

Constructive approximate interpolation by neural networks in the metric space. - Feilong Cao; Shaobo Lin; Zongben Xu (Math. Comput. Model., 2010)

Essential rate for approximation by spherical neural networks. - Shaobo Lin; Feilong Cao; Zongben Xu (Neural Networks, 2011)

A general radial quasi-interpolation operator on the sphere. - Shaobo Lin; Feilong Cao; Xiangyu Chang; Zongben Xu (J. Approx. Theory, 2012)

Approximation by neural networks with scattered data. - Shaobo Lin; Xiaofei Guo; Feilong Cao; Zongben Xu (Appl. Math. Comput., 2013)

Learning rates of lq coefficient regularization learning with Gaussian kernel. - Shaobo Lin; Jinshan Zeng; Jian Fang 001; Zongben Xu (CoRR, 2013)

Learning Capability of Relaxed Greedy Algorithms. - Shaobo Lin; Yuanhua Rong; Xingping Sun; Zongben Xu (IEEE Trans. Neural Networks Learn. Syst., 2013)

Learning and approximation capability of orthogonal super greedy algorithm. - Jian Fang 001; Shaobo Lin; Zongben Xu (CoRR, 2014)

Greedy metrics in orthogonal greedy learning. - Lin Xu 001; Shaobo Lin; Jinshan Zeng; Zongben Xu (CoRR, 2014)

Almost optimal estimates for approximation and learning by radial basis function networks. - Shaobo Lin; Xia Liu; Yuanhua Rong; Zongben Xu (Mach. Learn., 2014)

Learning Rates of lq Coefficient Regularization Learning with Gaussian Kernel. - Shaobo Lin; Jinshan Zeng; Jian Fang 001; Zongben Xu (Neural Comput., 2014)

Sparse solution of underdetermined linear equations via adaptively iterative thresholding. - Jinshan Zeng; Shaobo Lin; Zongben Xu (Signal Process., 2014)

L1/2 Regularization: Convergence of Iterative Half Thresholding Algorithm. - Jinshan Zeng; Shaobo Lin; Yao Wang 003; Zongben Xu (IEEE Trans. Signal Process., 2014)

Re-scale boosting for regression and classification. - Shaobo Lin; Yao Wang 003; Lin Xu 001 (CoRR, 2015)

Shrinkage degree in L2-re-scale boosting for regression. - Lin Xu 001; Shaobo Lin; Yao Wang 003; Zongben Xu (CoRR, 2015)

A Gauss-Seidel Iterative Thresholding Algorithm for lq Regularized Least Squares Regression. - Jinshan Zeng; Zhimin Peng; Shaobo Lin (CoRR, 2015)

Jackson-type inequalities for spherical neural networks with doubling weights. - Shaobo Lin; Jinshan Zeng; Lin Xu 001; Zongben Xu (Neural Networks, 2015)

Error Estimate for Spherical Neural Networks Interpolation. - Shaobo Lin; Jinshan Zeng; Zongben Xu (Neural Process. Lett., 2015)

Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II). - Shaobo Lin; Xia Liu; Jian Fang 001; Zongben Xu (IEEE Trans. Neural Networks Learn. Syst., 2015)

Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part I). - Xia Liu; Shaobo Lin; Jian Fang 001; Zongben Xu (IEEE Trans. Neural Networks Learn. Syst., 2015)

Learning capability of the truncated greedy algorithm. - Lin Xu 001; Shaobo Lin; Zongben Xu (Sci. China Inf. Sci., 2016)

Divide and Conquer Local Average Regression. - Xiangyu Chang; Shaobo Lin; Yao Wang 003 (CoRR, 2016)

Greedy Criterion in Orthogonal Greedy Learning. - Lin Xu 001; Shaobo Lin; Jinshan Zeng; Xia Liu; Zongben Xu (CoRR, 2016)

Simultaneous approximation by spherical neural networks. - Shaobo Lin; Feilong Cao (Neurocomputing, 2016)

Linear and nonlinear approximation of spherical radial basis function networks. - Shaobo Lin (J. Complex., 2016)

Learning and approximation capabilities of orthogonal super greedy algorithm. - Jian Fang 001; Shaobo Lin; Zongben Xu (Knowl. Based Syst., 2016)

Sparse Regularization: Convergence Of Iterative Jumping Thresholding Algorithm. - Jinshan Zeng; Shaobo Lin; Zongben Xu (IEEE Trans. Signal Process., 2016)

GAITA: A Gauss-Seidel iterative thresholding algorithm for ℓq regularized least squares regression. - Jinshan Zeng; Zhimin Peng; Shaobo Lin (J. Comput. Appl. Math., 2017)

Distributed Semi-supervised Learning with Kernel Ridge Regression. - Xiangyu Chang; Shaobo Lin; Ding-Xuan Zhou (J. Mach. Learn. Res., 2017)

Distributed Learning with Regularized Least Squares. - Shaobo Lin; Xin Guo 003; Ding-Xuan Zhou (J. Mach. Learn. Res., 2017)

Learning Rates for Classification with Gaussian Kernels. - Shaobo Lin; Jinshan Zeng; Xiangyu Chang (Neural Comput., 2017)

Limitations of shallow nets approximation. - Shaobo Lin (Neural Networks, 2017)

Shrinkage Degree in L2-Rescale Boosting for Regression. - Lin Xu 001; Shaobo Lin; Yao Wang 003; Zongben Xu (IEEE Trans. Neural Networks Learn. Syst., 2017)

Generalization Bounds for Regularized Pairwise Learning. - Yunwen Lei; Shao-Bo Lin; Ke Tang 001 (IJCAI, 2018)

Block Coordinate Descent for Deep Learning: Unified Convergence Guarantees. - Jinshan Zeng; Tim Tsz-Kit Lau; Shaobo Lin; Yuan Yao 011 (CoRR, 2018)

Construction of Neural Networks for Realization of Localized Deep Learning. - Charles K. Chui; Shao-Bo Lin; Ding-Xuan Zhou (Frontiers Appl. Math. Stat., 2018)

Corrigendum to "GAITA: A Gauss-Seidel iterative thresholding algorithm for lq regularized least squares regression" [J. Comput. Appl. Math. 319 (2017) 220-235]. - Jinshan Zeng; Zhimin Peng; Shaobo Lin (J. Comput. Appl. Math., 2018)

Greedy Criterion in Orthogonal Greedy Learning. - Lin Xu 001; Shaobo Lin; Jinshan Zeng; Xia Liu; Yi Fang 006; Zongben Xu (IEEE Trans. Cybern., 2018)

High-Resolution Driving Scene Synthesis Using Stacked Conditional Gans and Spectral Normalization. - Shaobo Lin; Long Chen 005; Qin Zou 001; Wei Tian 001 (ICME, 2019)

Global Convergence of Block Coordinate Descent in Deep Learning. - Jinshan Zeng; Tim Tsz-Kit Lau; Shaobo Lin; Yuan Yao 011 (ICML, 2019)

A Convergence Analysis of Nonlinearly Constrained ADMM in Deep Learning. - Jinshan Zeng; Shaobo Lin; Yuan Yao 011 (CoRR, 2019)

Deep Neural Networks for Rotation-Invariance Approximation and Learning. - Charles K. Chui; Shao-Bo Lin; Ding-Xuan Zhou (CoRR, 2019)

Fast Polynomial Kernel Classification for Massive Data. - Jinshan Zeng; Minrun Wu; Shao-Bo Lin; Ding-Xuan Zhou (CoRR, 2019)

Deep Net Tree Structure for Balance of Capacity and Approximation Ability. - Charles K. Chui; Shao-Bo Lin; Ding-Xuan Zhou (Frontiers Appl. Math. Stat., 2019)

Nonparametric regression using needlet kernels for spherical data. - Shao-Bo Lin (J. Complex., 2019)

Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping. - Shao-Bo Lin; Yunwen Lei; Ding-Xuan Zhou (J. Mach. Learn. Res., 2019)

Fast Learning With Polynomial Kernels. - Shaobo Lin; Jinshan Zeng (IEEE Trans. Cybern., 2019)

Constructive Neural Network Learning. - Shaobo Lin; Jinshan Zeng; Xiaoqin Zhang 002 (IEEE Trans. Cybern., 2019)

Unified Low-Rank Matrix Estimate via Penalized Matrix Least Squares Approximation. - Xiangyu Chang; Yan Zhong; Yao Wang 003; Shaobo Lin (IEEE Trans. Neural Networks Learn. Syst., 2019)

Generalization and Expressivity for Deep Nets. - Shao-Bo Lin (IEEE Trans. Neural Networks Learn. Syst., 2019)

Rescaled Boosting in Classification. - Yao Wang 003; Xu Liao; Shaobo Lin (IEEE Trans. Neural Networks Learn. Syst., 2019)

Distributed Learning with Dependent Samples. - Shao-Bo Lin (CoRR, 2020)

Kernel-based L_2-Boosting with Structure Constraints. - Yao Wang 003; Xin Guo 003; Shao-Bo Lin (CoRR, 2020)

Distributed Kernel Ridge Regression with Communications. - Shao-Bo Lin; Di Wang 008; Ding-Xuan Zhou (J. Mach. Learn. Res., 2020)

Learning Through Deterministic Assignment of Hidden Parameters. - Jian Fang 001; Shaobo Lin; Zongben Xu (IEEE Trans. Cybern., 2020)

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

Radial Basis Function Approximation with Distributively Stored Data on Spheres. - Han Feng; Shao-Bo Lin; Ding-Xuan Zhou (CoRR, 2021)

On ADMM in Deep Learning: Convergence and Saturation-Avoidance. - Jinshan Zeng; Shao-Bo Lin; Yuan Yao 011; Ding-Xuan Zhou (J. Mach. Learn. Res., 2021)

Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere. - Shaobo Lin; Yuguang Wang 001; Ding-Xuan Zhou (SIAM J. Numer. Anal., 2021)

Deep Neural Network Based Vehicle and Pedestrian Detection for Autonomous Driving: A Survey. - Long Chen 005; Shaobo Lin; Xiankai Lu; Dongpu Cao; Hangbin Wu; Chi Guo; Chun Liu 003; Fei-Yue Wang 001 (IEEE Trans. Intell. Transp. Syst., 2021)

Random Sketching for Neural Networks With ReLU. - Di Wang 008; Jinshan Zeng; Shao-Bo Lin (IEEE Trans. Neural Networks Learn. Syst., 2021)

Three-stage Training Pipeline with Patch Random Drop for Few-shot Object Detection. - Shaobo Lin; Xingyu Zeng; Shilin Yan; Rui Zhao (ACCV (6), 2022)

A Unified Framework with Meta-dropout for Few-shot Learning. - Shaobo Lin; Xingyu Zeng; Rui Zhao (CoRR, 2022)

Toward Efficient Ensemble Learning with Structure Constraints: Convergent Algorithms and Applications. - Shao-Bo Lin; Shaojie Tang 001; Yao Wang 003; Di Wang 008 (INFORMS J. Comput., 2022)

Nystrom Regularization for Time Series Forecasting. - Zirui Sun; Mingwei Dai; Yao Wang 003; Shao-Bo Lin (J. Mach. Learn. Res., 2022)

Fully corrective gradient boosting with squared hinge: Fast learning rates and early stopping. - Jinshan Zeng; Min Zhang; Shao-Bo Lin (Neural Networks, 2022)

Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. - Zhi Han; Siquan Yu; Shao-Bo Lin; Ding-Xuan Zhou (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Learning With Selected Features. - Shao-Bo Lin; Jian Fang 001; Xiangyu Chang (IEEE Trans. Cybern., 2022)

Universal Consistency of Deep Convolutional Neural Networks. - Shao-Bo Lin; Kaidong Wang; Yao Wang 003; Ding-Xuan Zhou (IEEE Trans. Inf. Theory, 2022)

Distributed Learning With Dependent Samples. - Zirui Sun; Shao-Bo Lin (IEEE Trans. Inf. Theory, 2022)

Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data. - Charles K. Chui; Shao-Bo Lin; Bo Zhang; Ding-Xuan Zhou (IEEE Trans. Neural Networks Learn. Syst., 2022)

Explore the Power of Synthetic Data on Few-shot Object Detection. - Shaobo Lin; Kun Wang; Xingyu Zeng; Rui Zhao (CVPR Workshops, 2023)

An Effective Crop-Paste Pipeline for Few-shot Object Detection. - Shaobo Lin; Kun Wang; Xingyu Zeng; Rui Zhao (CVPR Workshops, 2023)

Explore the Power of Dropout on Few-shot Learning. - Shaobo Lin; Xingyu Zeng; Rui Zhao (CoRR, 2023)

Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes. - Di Wang 008; Yao Wang 003; Shaojie Tang 001; Shao-Bo Lin (CoRR, 2023)

Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning. - Zhi Han; Baichen Liu; Shao-Bo Lin; Ding-Xuan Zhou (CoRR, 2023)

Optimal Approximation and Learning Rates for Deep Convolutional Neural Networks. - Shao-Bo Lin (CoRR, 2023)

Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos. - Di Wang 008; Xiaotong Liu; Shao-Bo Lin; Ding-Xuan Zhou (CoRR, 2023)

Distributed Uncertainty Quantification of Kernel Interpolation on Spheres. - Shao-Bo Lin; Xingping Sun; Di Wang 008 (CoRR, 2023)

Lifting the Veil: Unlocking the Power of Depth in Q-learning. - Shao-Bo Lin; Tao Li; Shaojie Tang 001; Yao Wang 003; Ding-Xuan Zhou (CoRR, 2023)

Adaptive Parameter Selection for Kernel Ridge Regression. - Shao-Bo Lin (CoRR, 2023)

Construction of Deep ReLU Nets for Spatially Sparse Learning. - Xia Liu; Di Wang 008; Shao-Bo Lin (IEEE Trans. Neural Networks Learn. Syst., 2023)

Integral Operator Approaches for Scattered Data Fitting on Spheres. - Shao-Bo Lin (CoRR, 2024)

Lepskii Principle for Distributed Kernel Ridge Regression. - Shao-Bo Lin (CoRR, 2024)

Component-based Sketching for Deep ReLU Nets. - Di Wang 008; Shao-Bo Lin; Deyu Meng; Feilong Cao (CoRR, 2024)

Weighted Spectral Filters for Kernel Interpolation on Spheres: Estimates of Prediction Accuracy for Noisy Data. - Xiaotong Liu; Jinxin Wang; Di Wang 008; Shao-Bo Lin (SIAM J. Imaging Sci., 2024)

Kernel Interpolation of High Dimensional Scattered Data. - Shao-Bo Lin; Xiangyu Chang; Xingping Sun (SIAM J. Numer. Anal., 2024)

Sketching with Spherical Designs for Noisy Data Fitting on Spheres. - Shao-Bo Lin; Di Wang 008; Ding-Xuan Zhou (SIAM J. Sci. Comput., 2024)

Feature Qualification by Deep Nets: A Constructive Approach. - Feilong Cao; Shao-Bo Lin (CoRR, 2025)

Generalization Performance of Empirical Risk Minimization on Over-Parameterized Deep ReLU Nets. - Shao-Bo Lin; Yao Wang 003; Ding-Xuan Zhou (IEEE Trans. Inf. Theory, 2025)

Striking the Perfect Balance: Preserving Privacy While Boosting Utility in Collaborative Medical Prediction Platforms. - Shao-Bo Lin; Xiaotong Liu; Yao Wang 003 (CoRR, 2025)