Shaobo Lin

According to our database, Shaobo Lin authored at least 89 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Sketching with Spherical Designs for Noisy Data Fitting on Spheres.

Shao-Bo Lin, Di Wang, Ding-Xuan Zhou

SIAM J. Sci. Comput., February, 2024

Kernel Interpolation of High Dimensional Scattered Data.

Shao-Bo Lin, Xiangyu Chang, Xingping Sun

SIAM J. Numer. Anal., 2024

Weighted Spectral Filters for Kernel Interpolation on Spheres: Estimates of Prediction Accuracy for Noisy Data.

Xiaotong Liu, Jinxin Wang, Di Wang, Shao-Bo Lin

SIAM J. Imaging Sci., 2024

Component-based Sketching for Deep ReLU Nets.

Di Wang, Shao-Bo Lin, Deyu Meng, Feilong Cao

CoRR, 2024

Lepskii Principle for Distributed Kernel Ridge Regression.

Shao-Bo Lin

CoRR, 2024

Integral Operator Approaches for Scattered Data Fitting on Spheres.

Shao-Bo Lin

CoRR, 2024

2023

Construction of Deep ReLU Nets for Spatially Sparse Learning.

Xia Liu, Di Wang, Shao-Bo Lin

IEEE Trans. Neural Networks Learn. Syst., October, 2023

Adaptive Parameter Selection for Kernel Ridge Regression.

Shao-Bo Lin

CoRR, 2023

Lifting the Veil: Unlocking the Power of Depth in Q-learning.

Shao-Bo Lin, Tao Li, Shaojie Tang, Yao Wang, Ding-Xuan Zhou

CoRR, 2023

Distributed Uncertainty Quantification of Kernel Interpolation on Spheres.

Shao-Bo Lin, Xingping Sun, Di Wang

CoRR, 2023

Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos.

Di Wang, Xiaotong Liu, Shao-Bo Lin, Ding-Xuan Zhou

CoRR, 2023

Optimal Approximation and Learning Rates for Deep Convolutional Neural Networks.

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

Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes.

Di Wang, Yao Wang, Shaojie Tang, Shao-Bo Lin

CoRR, 2023

Explore the Power of Dropout on Few-shot Learning.

Shaobo Lin, Xingyu Zeng, Rui Zhao

CoRR, 2023

An Effective Crop-Paste Pipeline for Few-shot Object Detection.

Shaobo Lin, Kun Wang, Xingyu Zeng, Rui Zhao

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Explore the Power of Synthetic Data on Few-shot Object Detection.

Shaobo Lin, Kun Wang, Xingyu Zeng, Rui Zhao

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

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

Distributed Learning With Dependent Samples.

Zirui Sun, Shao-Bo Lin

IEEE Trans. Inf. Theory, 2022

Universal Consistency of Deep Convolutional Neural Networks.

Shao-Bo Lin, Kaidong Wang, Yao Wang, Ding-Xuan Zhou

IEEE Trans. Inf. Theory, 2022

Learning With Selected Features.

Shao-Bo Lin, Jian Fang, Xiangyu Chang

IEEE Trans. Cybern., 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

Fully corrective gradient boosting with squared hinge: Fast learning rates and early stopping.

Jinshan Zeng, Min Zhang, Shao-Bo Lin

Neural Networks, 2022

Nystrom Regularization for Time Series Forecasting.

Zirui Sun, Mingwei Dai, Yao Wang, Shao-Bo Lin

J. Mach. Learn. Res., 2022

Toward Efficient Ensemble Learning with Structure Constraints: Convergent Algorithms and Applications.

Shao-Bo Lin, Shaojie Tang, Yao Wang, Di Wang

INFORMS J. Comput., 2022

A Unified Framework with Meta-dropout for Few-shot Learning.

Shaobo Lin, Xingyu Zeng, Rui Zhao

CoRR, 2022

Three-stage Training Pipeline with Patch Random Drop for Few-shot Object Detection.

Shaobo Lin, Xingyu Zeng, Shilin Yan, Rui Zhao

Proceedings of the Computer Vision - ACCV 2022, 2022

2021

Random Sketching for Neural Networks With ReLU.

Di Wang, Jinshan Zeng, Shao-Bo Lin

IEEE Trans. Neural Networks Learn. Syst., 2021

Deep Neural Network Based Vehicle and Pedestrian Detection for Autonomous Driving: A Survey.

Long Chen, Shaobo Lin, Xiankai Lu, Dongpu Cao, Hangbin Wu, Chi Guo, Chun Liu, Fei-Yue Wang

IEEE Trans. Intell. Transp. Syst., 2021

Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere.

Shaobo Lin, Yuguang Wang, Ding-Xuan Zhou

SIAM J. Numer. Anal., 2021

On ADMM in Deep Learning: Convergence and Saturation-Avoidance.

Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou

J. Mach. Learn. Res., 2021

Radial Basis Function Approximation with Distributively Stored Data on Spheres.

Han Feng, Shao-Bo Lin, Ding-Xuan Zhou

CoRR, 2021

Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets.

Shao-Bo Lin, Yao Wang, Ding-Xuan Zhou

CoRR, 2021

2020

Realizing Data Features by Deep Nets.

Zheng-Chu Guo, Lei Shi, Shao-Bo Lin

IEEE Trans. Neural Networks Learn. Syst., 2020

Learning Through Deterministic Assignment of Hidden Parameters.

Jian Fang, Shaobo Lin, Zongben Xu

IEEE Trans. Cybern., 2020

Distributed Kernel Ridge Regression with Communications.

Shao-Bo Lin, Di Wang, Ding-Xuan Zhou

J. Mach. Learn. Res., 2020

Kernel-based L_2-Boosting with Structure Constraints.

Yao Wang, Xin Guo, Shao-Bo Lin

CoRR, 2020

Distributed Learning with Dependent Samples.

Shao-Bo Lin

CoRR, 2020

2019

Rescaled Boosting in Classification.

Yao Wang, Xu Liao, 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

Unified Low-Rank Matrix Estimate via Penalized Matrix Least Squares Approximation.

Xiangyu Chang, Yan Zhong, Yao Wang, Shaobo Lin

IEEE Trans. Neural Networks Learn. Syst., 2019

Constructive Neural Network Learning.

Shaobo Lin, Jinshan Zeng, Xiaoqin Zhang

IEEE Trans. Cybern., 2019

Fast Learning With Polynomial Kernels.

Shaobo Lin, Jinshan Zeng

IEEE Trans. Cybern., 2019

Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping.

Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou

J. Mach. Learn. Res., 2019

Nonparametric regression using needlet kernels for spherical data.

Shao-Bo Lin

J. Complex., 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

Fast Polynomial Kernel Classification for Massive Data.

Jinshan Zeng, Minrun Wu, Shao-Bo Lin, Ding-Xuan Zhou

CoRR, 2019

Deep Neural Networks for Rotation-Invariance Approximation and Learning.

Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou

CoRR, 2019

A Convergence Analysis of Nonlinearly Constrained ADMM in Deep Learning.

Jinshan Zeng, Shaobo Lin, Yuan Yao

CoRR, 2019

Global Convergence of Block Coordinate Descent in Deep Learning.

Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao

Proceedings of the 36th International Conference on Machine Learning, 2019

High-Resolution Driving Scene Synthesis Using Stacked Conditional Gans and Spectral Normalization.

Shaobo Lin, Long Chen, Qin Zou, Wei Tian

Proceedings of the IEEE International Conference on Multimedia and Expo, 2019

2018

Greedy Criterion in Orthogonal Greedy Learning.

Lin Xu, Shaobo Lin, Jinshan Zeng, Xia Liu, Yi Fang, Zongben Xu

IEEE Trans. Cybern., 2018

Corrigendum to "GAITA: A Gauss-Seidel iterative thresholding algorithm for l_q regularized least squares regression" [J. Comput. Appl. Math. 319 (2017) 220-235].

Jinshan Zeng, Zhimin Peng, Shaobo Lin

J. Comput. Appl. Math., 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

Block Coordinate Descent for Deep Learning: Unified Convergence Guarantees.

Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao

CoRR, 2018

Generalization Bounds for Regularized Pairwise Learning.

Yunwen Lei, Shao-Bo Lin, Ke Tang

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2017

Shrinkage Degree in L₂-Rescale Boosting for Regression.

Lin Xu, Shaobo Lin, Yao Wang, Zongben Xu

IEEE Trans. Neural Networks Learn. Syst., 2017

Limitations of shallow nets approximation.

Shaobo Lin

Neural Networks, 2017

Learning Rates for Classification with Gaussian Kernels.

Shaobo Lin, Jinshan Zeng, Xiangyu Chang

Neural Comput., 2017

Distributed Learning with Regularized Least Squares.

Shaobo Lin, Xin Guo, Ding-Xuan Zhou

J. Mach. Learn. Res., 2017

Distributed Semi-supervised Learning with Kernel Ridge Regression.

Xiangyu Chang, Shaobo Lin, Ding-Xuan Zhou

J. Mach. Learn. Res., 2017

GAITA: A Gauss-Seidel iterative thresholding algorithm for ℓ_q regularized least squares regression.

Jinshan Zeng, Zhimin Peng, Shaobo Lin

J. Comput. Appl. Math., 2017

2016

Sparse Regularization: Convergence Of Iterative Jumping Thresholding Algorithm.

Jinshan Zeng, Shaobo Lin, Zongben Xu

IEEE Trans. Signal Process., 2016

Learning and approximation capabilities of orthogonal super greedy algorithm.

Jian Fang, Shaobo Lin, Zongben Xu

Knowl. Based Syst., 2016

Linear and nonlinear approximation of spherical radial basis function networks.

Shaobo Lin

J. Complex., 2016

Simultaneous approximation by spherical neural networks.

Shaobo Lin, Feilong Cao

Neurocomputing, 2016

Greedy Criterion in Orthogonal Greedy Learning.

Lin Xu, Shaobo Lin, Jinshan Zeng, Xia Liu, Zongben Xu

CoRR, 2016

Divide and Conquer Local Average Regression.

Xiangyu Chang, Shaobo Lin, Yao Wang

CoRR, 2016

Learning capability of the truncated greedy algorithm.

Lin Xu, Shaobo Lin, Zongben Xu

Sci. China Inf. Sci., 2016

2015

Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part I).

Xia Liu, Shaobo Lin, Jian Fang, Zongben Xu

IEEE Trans. Neural Networks Learn. Syst., 2015

Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II).

Shaobo Lin, Xia Liu, Jian Fang, Zongben Xu

IEEE Trans. Neural Networks Learn. Syst., 2015

Error Estimate for Spherical Neural Networks Interpolation.

Shaobo Lin, Jinshan Zeng, Zongben Xu

Neural Process. Lett., 2015

Jackson-type inequalities for spherical neural networks with doubling weights.

Shaobo Lin, Jinshan Zeng, Lin Xu, Zongben Xu

Neural Networks, 2015

A Gauss-Seidel Iterative Thresholding Algorithm for lq Regularized Least Squares Regression.

Jinshan Zeng, Zhimin Peng, Shaobo Lin

CoRR, 2015

Shrinkage degree in L₂-re-scale boosting for regression.

Lin Xu, Shaobo Lin, Yao Wang, Zongben Xu

CoRR, 2015

Re-scale boosting for regression and classification.

Shaobo Lin, Yao Wang, Lin Xu

CoRR, 2015

2014

L_1/2 Regularization: Convergence of Iterative Half Thresholding Algorithm.

Jinshan Zeng, Shaobo Lin, Yao Wang, Zongben Xu

IEEE Trans. Signal Process., 2014

Sparse solution of underdetermined linear equations via adaptively iterative thresholding.

Jinshan Zeng, Shaobo Lin, Zongben Xu

Signal Process., 2014

Learning Rates of <i>l^q</i> Coefficient Regularization Learning with Gaussian Kernel.

Shaobo Lin, Jinshan Zeng, Jian Fang, Zongben Xu

Neural Comput., 2014

Almost optimal estimates for approximation and learning by radial basis function networks.

Shaobo Lin, Xia Liu, Yuanhua Rong, Zongben Xu

Mach. Learn., 2014

Greedy metrics in orthogonal greedy learning.

Lin Xu, Shaobo Lin, Jinshan Zeng, Zongben Xu

CoRR, 2014

Learning and approximation capability of orthogonal super greedy algorithm.

Jian Fang, Shaobo Lin, Zongben Xu

CoRR, 2014

2013

Learning Capability of Relaxed Greedy Algorithms.

Shaobo Lin, Yuanhua Rong, Xingping Sun, Zongben Xu

IEEE Trans. Neural Networks Learn. Syst., 2013

Learning rates of l^q coefficient regularization learning with Gaussian kernel.

Shaobo Lin, Jinshan Zeng, Jian Fang, Zongben Xu

CoRR, 2013

Approximation by neural networks with scattered data.

Shaobo Lin, Xiaofei Guo, Feilong Cao, Zongben Xu

Appl. Math. Comput., 2013

2012

A general radial quasi-interpolation operator on the sphere.

Shaobo Lin, Feilong Cao, Xiangyu Chang, Zongben Xu

J. Approx. Theory, 2012

2011

Essential rate for approximation by spherical neural networks.

Shaobo Lin, Feilong Cao, Zongben Xu

Neural Networks, 2011

2010

Constructive approximate interpolation by neural networks in the metric space.

Feilong Cao, Shaobo Lin, Zongben Xu

Math. Comput. Model., 2010

Approximation capability of interpolation neural networks.

Feilong Cao, Shaobo Lin, Zongben Xu

Neurocomputing, 2010