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Shao-Bo Lin
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2020 – today
- 2024
[j55]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): 951-983 (2024)- [j54]Shao-Bo Lin, Xiangyu Chang, Xingping Sun:
Kernel Interpolation of High Dimensional Scattered Data. SIAM J. Numer. Anal. 62(3): 1098-1118 (2024) - [j53]Shao-Bo Lin, Di Wang, Ding-Xuan Zhou:
Sketching with Spherical Designs for Noisy Data Fitting on Spheres. SIAM J. Sci. Comput. 46(1): 313- (2024) - [i51]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 abs/2401.08364 (2024) - [i50]Shao-Bo Lin:
Integral Operator Approaches for Scattered Data Fitting on Spheres. CoRR abs/2401.15294 (2024) - [i49]Shao-Bo Lin:
Lepskii Principle for Distributed Kernel Ridge Regression. CoRR abs/2409.05070 (2024) - [i48]Di Wang, Shao-Bo Lin, Deyu Meng, Feilong Cao:
Component-based Sketching for Deep ReLU Nets. CoRR abs/2409.14174 (2024) - 2023
- [j52]Xia Liu, Di Wang, Shao-Bo Lin:
Construction of Deep ReLU Nets for Spatially Sparse Learning. IEEE Trans. Neural Networks Learn. Syst. 34(10): 7746-7760 (2023) - [c6]Shaobo Lin, Kun Wang, Xingyu Zeng, Rui Zhao:
Explore the Power of Synthetic Data on Few-shot Object Detection. CVPR Workshops 2023: 638-647 - [c5]Shaobo Lin, Kun Wang, Xingyu Zeng, Rui Zhao:
An Effective Crop-Paste Pipeline for Few-shot Object Detection. CVPR Workshops 2023: 4820-4828 - [i47]Shaobo Lin, Xingyu Zeng, Rui Zhao:
Explore the Power of Dropout on Few-shot Learning. CoRR abs/2301.11015 (2023) - [i46]Di Wang, Yao Wang, Shaojie Tang, Shao-Bo Lin:
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes. CoRR abs/2302.10434 (2023) - [i45]Shaobo Lin, Kun Wang, Xingyu Zeng, Rui Zhao:
An Effective Crop-Paste Pipeline for Few-shot Object Detection. CoRR abs/2302.14452 (2023) - [i44]Shaobo Lin, Di Wang, Ding-Xuan Zhou:
Sketching with Spherical Designs for Noisy Data Fitting on Spheres. CoRR abs/2303.04550 (2023) - [i43]Shaobo Lin, Kun Wang, Xingyu Zeng, Rui Zhao:
Explore the Power of Synthetic Data on Few-shot Object Detection. CoRR abs/2303.13221 (2023) - [i42]Zhi Han, Baichen Liu, Shao-Bo Lin, Ding-Xuan Zhou:
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning. CoRR abs/2307.16203 (2023) - [i41]Shao-Bo Lin:
Optimal Approximation and Learning Rates for Deep Convolutional Neural Networks. CoRR abs/2308.03259 (2023) - [i40]Di Wang, Xiaotong Liu, Shao-Bo Lin, Ding-Xuan Zhou:
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos. CoRR abs/2309.04236 (2023) - [i39]Shao-Bo Lin, Xingping Sun, Di Wang:
Distributed Uncertainty Quantification of Kernel Interpolation on Spheres. CoRR abs/2310.16384 (2023) - [i38]Shao-Bo Lin, Tao Li, Shaojie Tang, Yao Wang, Ding-Xuan Zhou:
Lifting the Veil: Unlocking the Power of Depth in Q-learning. CoRR abs/2310.17915 (2023) - [i37]Shao-Bo Lin:
Adaptive Parameter Selection for Kernel Ridge Regression. CoRR abs/2312.05885 (2023) - 2022
- [j51]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): 3096-3116 (2022) - [j50]Zirui Sun, Mingwei Dai, Yao Wang, Shao-Bo Lin:
Nystrom Regularization for Time Series Forecasting. J. Mach. Learn. Res. 23: 312:1-312:42 (2022) - [j49]Jinshan Zeng, Min Zhang, Shao-Bo Lin:
Fully corrective gradient boosting with squared hinge: Fast learning rates and early stopping. Neural Networks 147: 136-151 (2022) - [j48]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): 1853-1868 (2022) - [j47]Shao-Bo Lin, Jian Fang, Xiangyu Chang:
Learning With Selected Features. IEEE Trans. Cybern. 52(4): 2032-2046 (2022) - [j46]Shao-Bo Lin, Kaidong Wang, Yao Wang, Ding-Xuan Zhou:
Universal Consistency of Deep Convolutional Neural Networks. IEEE Trans. Inf. Theory 68(7): 4610-4617 (2022) - [j45]Zirui Sun, Shao-Bo Lin:
Distributed Learning With Dependent Samples. IEEE Trans. Inf. Theory 68(9): 6003-6020 (2022) - [j44]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): 229-243 (2022) - [c4]Shaobo Lin, Xingyu Zeng, Shilin Yan, Rui Zhao:
Three-stage Training Pipeline with Patch Random Drop for Few-shot Object Detection. ACCV (6) 2022: 286-302 - [i36]Shaobo Lin, Xingyu Zeng, Rui Zhao:
A Unified Framework with Meta-dropout for Few-shot Learning. CoRR abs/2210.06409 (2022) - 2021
- [j43]Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou:
On ADMM in Deep Learning: Convergence and Saturation-Avoidance. J. Mach. Learn. Res. 22: 199:1-199:67 (2021) - [j42]Shaobo Lin, Yuguang Wang, Ding-Xuan Zhou:
Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere. SIAM J. Numer. Anal. 59(2): 634-659 (2021) - [j41]Long Chen, Shaobo Lin, Xiankai Lu, Dongpu Cao, Hangbin Wu, Chi Guo, Chun Liu, Fei-Yue Wang:
Deep Neural Network Based Vehicle and Pedestrian Detection for Autonomous Driving: A Survey. IEEE Trans. Intell. Transp. Syst. 22(6): 3234-3246 (2021) - [j40]Di Wang, Jinshan Zeng, Shao-Bo Lin:
Random Sketching for Neural Networks With ReLU. IEEE Trans. Neural Networks Learn. Syst. 32(2): 748-762 (2021) - [i35]Shao-Bo Lin, Kaidong Wang, Yao Wang, Ding-Xuan Zhou:
Universal Consistency of Deep Convolutional Neural Networks. CoRR abs/2106.12498 (2021) - [i34]Zirui Sun, Mingwei Dai, Yao Wang, Shao-Bo Lin:
Nyström Regularization for Time Series Forecasting. CoRR abs/2111.07109 (2021) - [i33]Shao-Bo Lin, Yao Wang, Ding-Xuan Zhou:
Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets. CoRR abs/2111.14039 (2021) - [i32]Han Feng, Shao-Bo Lin, Ding-Xuan Zhou:
Radial Basis Function Approximation with Distributively Stored Data on Spheres. CoRR abs/2112.02499 (2021) - 2020
- [j39]Shao-Bo Lin, Di Wang, Ding-Xuan Zhou:
Distributed Kernel Ridge Regression with Communications. J. Mach. Learn. Res. 21: 93:1-93:38 (2020) - [j38]Jian Fang, Shaobo Lin, Zongben Xu:
Learning Through Deterministic Assignment of Hidden Parameters. IEEE Trans. Cybern. 50(5): 2321-2334 (2020) - [j37]Zheng-Chu Guo, Lei Shi, Shao-Bo Lin:
Realizing Data Features by Deep Nets. IEEE Trans. Neural Networks Learn. Syst. 31(10): 4036-4048 (2020) - [i31]Shao-Bo Lin:
Distributed Learning with Dependent Samples. CoRR abs/2002.03757 (2020) - [i30]Shao-Bo Lin, Di Wang, Ding-Xuan Zhou:
Distributed Kernel Ridge Regression with Communications. CoRR abs/2003.12210 (2020) - [i29]Jinshan Zeng, Min Zhang, Shao-Bo Lin:
Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping. CoRR abs/2004.00179 (2020) - [i28]Zhi Han, Siquan Yu, Shao-Bo Lin, Ding-Xuan Zhou:
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. CoRR abs/2004.00245 (2020) - [i27]Shao-Bo Lin, Xiangyu Chang, Xingping Sun:
Kernel Interpolation of High Dimensional Scattered Data. CoRR abs/2009.01514 (2020) - [i26]Yao Wang, Xin Guo, Shao-Bo Lin:
Kernel-based L_2-Boosting with Structure Constraints. CoRR abs/2009.07558 (2020)
2010 – 2019
- 2019
- [j36]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: 46 (2019) - [j35]Shao-Bo Lin:
Nonparametric regression using needlet kernels for spherical data. J. Complex. 50: 66-83 (2019) - [j34]Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou:
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping. J. Mach. Learn. Res. 20: 46:1-46:36 (2019) - [j33]Shaobo Lin, Jinshan Zeng, Xiaoqin Zhang:
Constructive Neural Network Learning. IEEE Trans. Cybern. 49(1): 221-232 (2019) - [j32]Shaobo Lin, Jinshan Zeng:
Fast Learning With Polynomial Kernels. IEEE Trans. Cybern. 49(10): 3780-3792 (2019) - [j31]Xiangyu Chang, Yan Zhong, Yao Wang, Shaobo Lin:
Unified Low-Rank Matrix Estimate via Penalized Matrix Least Squares Approximation. IEEE Trans. Neural Networks Learn. Syst. 30(2): 474-485 (2019) - [j30]Shao-Bo Lin:
Generalization and Expressivity for Deep Nets. IEEE Trans. Neural Networks Learn. Syst. 30(5): 1392-1406 (2019) - [j29]Yao Wang, Xu Liao, Shaobo Lin:
Rescaled Boosting in Classification. IEEE Trans. Neural Networks Learn. Syst. 30(9): 2598-2610 (2019) - [c3]Shaobo Lin, Long Chen, Qin Zou, Wei Tian:
High-Resolution Driving Scene Synthesis Using Stacked Conditional Gans and Spectral Normalization. ICME 2019: 1330-1335 - [c2]Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao:
Global Convergence of Block Coordinate Descent in Deep Learning. ICML 2019: 7313-7323 - [i25]Zheng-Chu Guo, Lei Shi, Shao-Bo Lin:
Realizing data features by deep nets. CoRR abs/1901.00130 (2019) - [i24]Jinshan Zeng, Shaobo Lin, Yuan Yao:
A Convergence Analysis of Nonlinearly Constrained ADMM in Deep Learning. CoRR abs/1902.02060 (2019) - [i23]Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou:
Deep Neural Networks for Rotation-Invariance Approximation and Learning. CoRR abs/1904.01814 (2019) - [i22]Shao-Bo Lin, Yu Guang Wang, Ding-Xuan Zhou:
Distributed filtered hyperinterpolation for noisy data on the sphere. CoRR abs/1910.02434 (2019) - [i21]Jinshan Zeng, Minrun Wu, Shao-Bo Lin, Ding-Xuan Zhou:
Fast Polynomial Kernel Classification for Massive Data. CoRR abs/1911.10558 (2019) - [i20]Charles K. Chui, Shao-Bo Lin, Bo Zhang, Ding-Xuan Zhou:
Realization of spatial sparseness by deep ReLU nets with massive data. CoRR abs/1912.07464 (2019) - 2018
- [j28]Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou:
Construction of Neural Networks for Realization of Localized Deep Learning. Frontiers Appl. Math. Stat. 4: 14 (2018) - [j27]Jinshan Zeng, Zhimin Peng, Shaobo Lin:
Corrigendum to "GAITA: A Gauss-Seidel iterative thresholding algorithm for lq regularized least squares regression" [J. Comput. Appl. Math. 319 (2017) 220-235]. J. Comput. Appl. Math. 333: 458 (2018) - [j26]Lin Xu, Shaobo Lin, Jinshan Zeng, Xia Liu, Yi Fang, Zongben Xu:
Greedy Criterion in Orthogonal Greedy Learning. IEEE Trans. Cybern. 48(3): 955-966 (2018) - [c1]Yunwen Lei, Shao-Bo Lin, Ke Tang:
Generalization Bounds for Regularized Pairwise Learning. IJCAI 2018: 2376-2382 - [i19]Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao:
Block Coordinate Descent for Deep Learning: Unified Convergence Guarantees. CoRR abs/1803.00225 (2018) - [i18]Charles K. Chui, Shaobo Lin, Ding-Xuan Zhou:
Construction of neural networks for realization of localized deep learning. CoRR abs/1803.03503 (2018) - [i17]Shao-Bo Lin:
Generalization and Expressivity for Deep Nets. CoRR abs/1803.03772 (2018) - [i16]Jian Fang, Shaobo Lin, Zongben Xu:
Learning through deterministic assignment of hidden parameters. CoRR abs/1803.08374 (2018) - 2017
- [j25]Jinshan Zeng, Zhimin Peng, Shaobo Lin:
GAITA: A Gauss-Seidel iterative thresholding algorithm for ℓq regularized least squares regression. J. Comput. Appl. Math. 319: 220-235 (2017) - [j24]Xiangyu Chang, Shaobo Lin, Ding-Xuan Zhou:
Distributed Semi-supervised Learning with Kernel Ridge Regression. J. Mach. Learn. Res. 18: 46:1-46:22 (2017) - [j23]Shaobo Lin, Xin Guo, Ding-Xuan Zhou:
Distributed Learning with Regularized Least Squares. J. Mach. Learn. Res. 18: 92:1-92:31 (2017) - [j22]Shaobo Lin, Jinshan Zeng, Xiangyu Chang:
Learning Rates for Classification with Gaussian Kernels. Neural Comput. 29(12) (2017) - [j21]Shaobo Lin:
Limitations of shallow nets approximation. Neural Networks 94: 96-102 (2017) - [j20]Lin Xu, Shaobo Lin, Yao Wang, Zongben Xu:
Shrinkage Degree in L2-Rescale Boosting for Regression. IEEE Trans. Neural Networks Learn. Syst. 28(8): 1851-1864 (2017) - [i15]Shaobo Lin, Jinshan Zeng, Xiangyu Chang:
Learning rates for classification with Gaussian kernels. CoRR abs/1702.08701 (2017) - 2016
- [j19]Lin Xu, Shaobo Lin, Zongben Xu:
Learning capability of the truncated greedy algorithm. Sci. China Inf. Sci. 59(5): 052103:1-052103:15 (2016) - [j18]Shaobo Lin, Feilong Cao:
Simultaneous approximation by spherical neural networks. Neurocomputing 175: 348-354 (2016) - [j17]Shaobo Lin:
Linear and nonlinear approximation of spherical radial basis function networks. J. Complex. 35: 86-101 (2016) - [j16]Jian Fang, Shaobo Lin, Zongben Xu:
Learning and approximation capabilities of orthogonal super greedy algorithm. Knowl. Based Syst. 95: 86-98 (2016) - [j15]Jinshan Zeng, Shaobo Lin, Zongben Xu:
Sparse Regularization: Convergence Of Iterative Jumping Thresholding Algorithm. IEEE Trans. Signal Process. 64(19): 5106-5118 (2016) - [i14]Xiangyu Chang, Shaobo Lin, Yao Wang:
Divide and Conquer Local Average Regression. CoRR abs/1601.06239 (2016) - [i13]Lin Xu, Shaobo Lin, Jinshan Zeng, Xia Liu, Zongben Xu:
Greedy Criterion in Orthogonal Greedy Learning. CoRR abs/1604.05993 (2016) - [i12]Shaobo Lin, Jinshan Zeng, Xiaoqin Zhang:
Constructive neural network learning. CoRR abs/1605.00079 (2016) - [i11]Shaobo Lin, Xin Guo, Ding-Xuan Zhou:
Distributed Learning with Regularized Least Squares. CoRR abs/1608.03339 (2016) - 2015
- [j14]Shaobo Lin, Jinshan Zeng, Lin Xu, Zongben Xu:
Jackson-type inequalities for spherical neural networks with doubling weights. Neural Networks 63: 57-65 (2015) - [j13]Shaobo Lin, Jinshan Zeng, Zongben Xu:
Error Estimate for Spherical Neural Networks Interpolation. Neural Process. Lett. 42(2): 369-379 (2015) - [j12]Xia Liu, Shaobo Lin, Jian Fang, Zongben Xu:
Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part I). IEEE Trans. Neural Networks Learn. Syst. 26(1): 7-20 (2015) - [j11]Shaobo Lin, Xia Liu, Jian Fang, Zongben Xu:
Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II). IEEE Trans. Neural Networks Learn. Syst. 26(1): 21-34 (2015) - [i10]Shaobo Lin:
Nonparametric regression using needlet kernels for spherical data. CoRR abs/1502.04168 (2015) - [i9]Shaobo Lin, Yao Wang, Lin Xu:
Re-scale boosting for regression and classification. CoRR abs/1505.01371 (2015) - [i8]Lin Xu, Shaobo Lin, Yao Wang, Zongben Xu:
Shrinkage degree in L2-re-scale boosting for regression. CoRR abs/1505.04369 (2015) - [i7]Jinshan Zeng, Zhimin Peng, Shaobo Lin:
A Gauss-Seidel Iterative Thresholding Algorithm for lq Regularized Least Squares Regression. CoRR abs/1507.03173 (2015) - 2014
- [j10]Shaobo Lin, Xia Liu, Yuanhua Rong, Zongben Xu:
Almost optimal estimates for approximation and learning by radial basis function networks. Mach. Learn. 95(2): 147-164 (2014) - [j9]Shaobo Lin, Jinshan Zeng, Jian Fang, Zongben Xu:
Learning Rates of lq Coefficient Regularization Learning with Gaussian Kernel. Neural Comput. 26(10): 2350-2378 (2014) - [j8]Jinshan Zeng, Shaobo Lin, Zongben Xu:
Sparse solution of underdetermined linear equations via adaptively iterative thresholding. Signal Process. 97: 152-161 (2014) - [j7]Jinshan Zeng, Shaobo Lin, Yao Wang, Zongben Xu:
L1/2 Regularization: Convergence of Iterative Half Thresholding Algorithm. IEEE Trans. Signal Process. 62(9): 2317-2329 (2014) - [i6]Shaobo Lin, Xia Liu, Jian Fang, Zongben Xu:
Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II). CoRR abs/1401.6240 (2014) - [i5]Jian Fang, Shaobo Lin, Zongben Xu:
Learning and approximation capability of orthogonal super greedy algorithm. CoRR abs/1409.5330 (2014) - [i4]Lin Xu, Shaobo Lin, Jinshan Zeng, Zongben Xu:
Greedy metrics in orthogonal greedy learning. CoRR abs/1411.3553 (2014) - 2013
- [j6]Shaobo Lin, Xiaofei Guo, Feilong Cao, Zongben Xu:
Approximation by neural networks with scattered data. Appl. Math. Comput. 224: 29-35 (2013) - [j5]Shaobo Lin, Yuanhua Rong, Xingping Sun, Zongben Xu:
Learning Capability of Relaxed Greedy Algorithms. IEEE Trans. Neural Networks Learn. Syst. 24(10): 1598-1608 (2013) - [i3]Jinshan Zeng, Shaobo Lin, Zongben Xu:
Sparse Solution of Underdetermined Linear Equations via Adaptively Iterative Thresholding. CoRR abs/1310.3954 (2013) - [i2]Jinshan Zeng, Shaobo Lin, Yao Wang, Zongben Xu:
L1/2 Regularization: Convergence of Iterative Half Thresholding Algorithm. CoRR abs/1311.0156 (2013) - [i1]Shaobo Lin, Jinshan Zeng, Jian Fang, Zongben Xu:
Learning rates of lq coefficient regularization learning with Gaussian kernel. CoRR abs/1312.5465 (2013) - 2012
- [j4]Shaobo Lin, Feilong Cao, Xiangyu Chang, Zongben Xu:
A general radial quasi-interpolation operator on the sphere. J. Approx. Theory 164(10): 1402-1414 (2012) - 2011
- [j3]Shaobo Lin, Feilong Cao, Zongben Xu:
Essential rate for approximation by spherical neural networks. Neural Networks 24(7): 752-758 (2011) - 2010
- [j2]Feilong Cao, Shaobo Lin, Zongben Xu:
Approximation capability of interpolation neural networks. Neurocomputing 74(1-3): 457-460 (2010) - [j1]Feilong Cao, Shaobo Lin, Zongben Xu:
Constructive approximate interpolation by neural networks in the metric space. Math. Comput. Model. 52(9-10): 1674-1681 (2010)
Coauthor Index
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