Shao-Bo Lin

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 48

Top Topics

Kernel Ridge Regression

Top Venues

IEEE Trans. Neural Networks Learn. Syst.

J. Mach. Learn. Res.

IEEE Trans. Inf. Theory

Publications

Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
Sketching with Spherical Designs for Noisy Data Fitting on Spheres. SIAM J. Sci. Comput. 46 (1) (2024)
Shao-Bo Lin, Xiangyu Chang, Xingping Sun
Kernel Interpolation of High Dimensional Scattered Data. SIAM J. Numer. Anal. 62 (3) (2024)
Xiaotong Liu, Jinxin Wang, Di Wang, Shao-Bo Lin
Weighted Spectral Filters for Kernel Interpolation on Spheres: Estimates of Prediction Accuracy for Noisy Data. SIAM J. Imaging Sci. 17 (2) (2024)
Shao-Bo Lin
Integral Operator Approaches for Scattered Data Fitting on Spheres. CoRR (2024)
Xiaotong Liu, Jinxin Wang, Di Wang, Shao-Bo Lin
Weighted Spectral Filters for Kernel Interpolation on Spheres: Estimates of Prediction Accuracy for Noisy Data. CoRR (2024)
Xia Liu, Di Wang, Shao-Bo Lin
Construction of Deep ReLU Nets for Spatially Sparse Learning. IEEE Trans. Neural Networks Learn. Syst. 34 (10) (2023)
Shao-Bo Lin
Adaptive Parameter Selection for Kernel Ridge Regression. CoRR (2023)
Shao-Bo Lin, Tao Li, Shaojie Tang, Yao Wang, Ding-Xuan Zhou
Lifting the Veil: Unlocking the Power of Depth in Q-learning. CoRR (2023)
Shao-Bo Lin
Optimal Approximation and Learning Rates for Deep Convolutional Neural Networks. CoRR (2023)
Shao-Bo Lin, Xingping Sun, Di Wang
Distributed Uncertainty Quantification of Kernel Interpolation on Spheres. CoRR (2023)
Di Wang, Xiaotong Liu, Shao-Bo Lin, Ding-Xuan Zhou
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos. CoRR (2023)
Di Wang, Yao Wang, Shaojie Tang, Shao-Bo Lin
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes. CoRR (2023)
Zhi Han, Baichen Liu, Shao-Bo Lin, Ding-Xuan Zhou
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning. CoRR (2023)
Zirui Sun, Mingwei Dai, Yao Wang, Shao-Bo Lin
Nystrom Regularization for Time Series Forecasting. J. Mach. Learn. Res. 23 (2022)
Shao-Bo Lin, Kaidong Wang, Yao Wang, Ding-Xuan Zhou
Universal Consistency of Deep Convolutional Neural Networks. IEEE Trans. Inf. Theory 68 (7) (2022)
Shao-Bo Lin, Jian Fang, Xiangyu Chang
Learning With Selected Features. IEEE Trans. Cybern. 52 (4) (2022)
Shao-Bo Lin, Shaojie Tang, Yao Wang, Di Wang
Toward Efficient Ensemble Learning with Structure Constraints: Convergent Algorithms and Applications. INFORMS J. Comput. 34 (6) (2022)
Jinshan Zeng, Min Zhang, Shao-Bo Lin
Fully corrective gradient boosting with squared hinge: Fast learning rates and early stopping. Neural Networks 147 (2022)
Zirui Sun, Shao-Bo Lin
Distributed Learning With Dependent Samples. IEEE Trans. Inf. Theory 68 (9) (2022)
Charles K. Chui, Shao-Bo Lin, Bo Zhang, Ding-Xuan Zhou
Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data. IEEE Trans. Neural Networks Learn. Syst. 33 (1) (2022)
Zhi Han, Siquan Yu, Shao-Bo Lin, Ding-Xuan Zhou
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. IEEE Trans. Pattern Anal. Mach. Intell. 44 (4) (2022)
Shao-Bo Lin, Kaidong Wang, Yao Wang, Ding-Xuan Zhou
Universal Consistency of Deep Convolutional Neural Networks. CoRR (2021)
Zirui Sun, Mingwei Dai, Yao Wang, Shao-Bo Lin
Nyström Regularization for Time Series Forecasting. CoRR (2021)
Han Feng, Shao-Bo Lin, Ding-Xuan Zhou
Radial Basis Function Approximation with Distributively Stored Data on Spheres. CoRR (2021)
Di Wang, Jinshan Zeng, Shao-Bo Lin
Random Sketching for Neural Networks With ReLU. IEEE Trans. Neural Networks Learn. Syst. 32 (2) (2021)
Shao-Bo Lin, Yao Wang, Ding-Xuan Zhou
Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets. CoRR (2021)
Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou
On ADMM in Deep Learning: Convergence and Saturation-Avoidance. J. Mach. Learn. Res. 22 (2021)
Xiangyu Chang, Shao-Bo Lin
Adaptive Stopping Rule for Kernel-based Gradient Descent Algorithms. CoRR (2020)
Shao-Bo Lin, Xiangyu Chang, Xingping Sun
Kernel Interpolation of High Dimensional Scattered Data. CoRR (2020)
Zhi Han, Siquan Yu, Shao-Bo Lin, Ding-Xuan Zhou
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. CoRR (2020)
Zheng-Chu Guo, Lei Shi, Shao-Bo Lin
Realizing Data Features by Deep Nets. IEEE Trans. Neural Networks Learn. Syst. 31 (10) (2020)
Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
Distributed Kernel Ridge Regression with Communications. J. Mach. Learn. Res. 21 (2020)
Shao-Bo Lin
Distributed Learning with Dependent Samples. CoRR (2020)
Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
Distributed Kernel Ridge Regression with Communications. CoRR (2020)
Jinshan Zeng, Min Zhang, Shao-Bo Lin
Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping. CoRR (2020)
Yao Wang, Xin Guo, Shao-Bo Lin
Kernel-based L_2-Boosting with Structure Constraints. CoRR (2020)
Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
Deep Neural Networks for Rotation-Invariance Approximation and Learning. CoRR (2019)
Charles K. Chui, Shao-Bo Lin, Bo Zhang, Ding-Xuan Zhou
Realization of spatial sparseness by deep ReLU nets with massive data. CoRR (2019)
Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping. J. Mach. Learn. Res. 20 (2019)
Shao-Bo Lin
Generalization and Expressivity for Deep Nets. IEEE Trans. Neural Networks Learn. Syst. 30 (5) (2019)
Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
Deep Net Tree Structure for Balance of Capacity and Approximation Ability. Frontiers Appl. Math. Stat. 5 (2019)
Shao-Bo Lin, Yu Guang Wang, Ding-Xuan Zhou
Distributed filtered hyperinterpolation for noisy data on the sphere. CoRR (2019)
Zheng-Chu Guo, Lei Shi, Shao-Bo Lin
Realizing data features by deep nets. CoRR (2019)
Shao-Bo Lin
Nonparametric regression using needlet kernels for spherical data. J. Complex. 50 (2019)
Jinshan Zeng, Minrun Wu, Shao-Bo Lin, Ding-Xuan Zhou
Fast Polynomial Kernel Classification for Massive Data. CoRR (2019)
Yunwen Lei, Shao-Bo Lin, Ke Tang
Generalization Bounds for Regularized Pairwise Learning. IJCAI (2018)
Shao-Bo Lin
Generalization and Expressivity for Deep Nets. CoRR (2018)
Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
Construction of Neural Networks for Realization of Localized Deep Learning. Frontiers Appl. Math. Stat. 4 (2018)