18 citations · 52 across the 23 of their papers we have counts for
24 papers
Beyond Cross-Validation: Adaptive Parameter Selection for Kernel-Based Gradient Descents
Xiaotong Liu, Yunwen Lei, Xiangyu Chang +1
This paper proposes a novel parameter selection strategy for kernel-based gradient descent (KGD) algorithms, integrating bias-variance analysis with the splitting method. We introd…
Lifting the Veil: Unlocking the Power of Depth in Q-learning
Shao-Bo Lin, Tao Li, Shaojie Tang +2
With the help of massive data and rich computational resources, deep Q-learning has been widely used in operations research and management science and has contributed to great succ…
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
Di Wang, Xiaotong Liu, Shao-Bo Lin +1
Data silos, mainly caused by privacy and interoperability, significantly constrain collaborations among different organizations with similar data for the same purpose. Distributed…
Optimal Approximation and Learning Rates for Deep Convolutional Neural Networks
Shao-Bo Lin
This paper focuses on approximation and learning performance analysis for deep convolutional neural networks with zero-padding and max-pooling. We prove that, to approximate -sm…
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning
Zhi Han, Baichen Liu, Shao-Bo Lin +1
This paper studies the performance of deep convolutional neural networks (DCNNs) with zero-padding in feature extraction and learning. After verifying the roles of zero-padding in…
Sketching with Spherical Designs for Noisy Data Fitting on Spheres
Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
This paper proposes a sketching strategy based on spherical designs, which is applied to the classical spherical basis function approach for massive spherical data fitting. We cond…
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes
Di Wang, Yao Wang, Shao-Bo Lin
In recent years, large amounts of electronic health records (EHRs) concerning chronic diseases have been collected to facilitate medical diagnosis. Modeling the dynamic properties…
Radial Basis Function Approximation with Distributively Stored Data on Spheres
Han Feng, Shao-Bo Lin, Ding-Xuan Zhou
This paper proposes a distributed weighted regularized least squares algorithm (DWRLS) based on spherical radial basis functions and spherical quadrature rules to tackle spherical…
Nyström Regularization for Time Series Forecasting
Zirui Sun, Mingwei Dai, Yao Wang +1
This paper focuses on learning rate analysis of Nyström regularization with sequential sub-sampling for -mixing time series. Using a recently developed Banach-valued Bernstein i…
Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets
Shao-Bo Lin, Yao Wang, Ding-Xuan Zhou
In this paper, we study the generalization performance of global minima for implementing empirical risk minimization (ERM) on over-parameterized deep ReLU nets. Using a novel deepe…
Universal Consistency of Deep Convolutional Neural Networks
Shao-Bo Lin, Kaidong Wang, Yao Wang +1
Compared with avid research activities of deep convolutional neural networks (DCNNs) in practice, the study of theoretical behaviors of DCNNs lags heavily behind. In particular, th…
Kernel-based L_2-Boosting with Structure Constraints
Yao Wang, Xin Guo, Shao-Bo Lin
Developing efficient kernel methods for regression is very popular in the past decade. In this paper, utilizing boosting on kernel-based weaker learners, we propose a novel kernel-…
Kernel Interpolation of High Dimensional Scattered Data
Shao-Bo Lin, Xiangyu Chang, Xingping Sun
Data sites selected from modeling high-dimensional problems often appear scattered in non-paternalistic ways. Except for sporadic clustering at some spots, they become relatively f…
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization
Zhi Han, Siquan Yu, Shao-Bo Lin +1
Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques…
Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping
Jinshan Zeng, Min Zhang, Shao-Bo Lin
Boosting is a well-known method for improving the accuracy of weak learners in machine learning. However, its theoretical generalization guarantee is missing in literature. In this…
Distributed Kernel Ridge Regression with Communications
Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
This paper focuses on generalization performance analysis for distributed algorithms in the framework of learning theory. Taking distributed kernel ridge regression (DKRR) for exam…
Distributed Learning with Dependent Samples
Zirui Sun, Shao-Bo Lin
This paper focuses on learning rate analysis of distributed kernel ridge regression for strong mixing sequences. Using a recently developed integral operator approach and a classic…
Adaptive Stopping Rule for Kernel-based Gradient Descent Algorithms
Xiangyu Chang, Shao-Bo Lin
In this paper, we propose an adaptive stopping rule for kernel-based gradient descent (KGD) algorithms. We introduce the empirical effective dimension to quantify the increments of…
Realization of spatial sparseness by deep ReLU nets with massive data
Charles K. Chui, Shao-Bo Lin, Bo Zhang +1
The great success of deep learning poses urgent challenges for understanding its working mechanism and rationality. The depth, structure, and massive size of the data are recognize…
Fast Polynomial Kernel Classification for Massive Data
Jinshan Zeng, Minrun Wu, Shao-Bo Lin +1
In the era of big data, it is desired to develop efficient machine learning algorithms to tackle massive data challenges such as storage bottleneck, algorithmic scalability, and in…
Distributed filtered hyperinterpolation for noisy data on the sphere
Shao-Bo Lin, Yu Guang Wang, Ding-Xuan Zhou
Problems in astrophysics, space weather research and geophysics usually need to analyze noisy big data on the sphere. This paper develops distributed filtered hyperinterpolation fo…
Deep Neural Networks for Rotation-Invariance Approximation and Learning
Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
Based on the tree architecture, the objective of this paper is to design deep neural networks with two or more hidden layers (called deep nets) for realization of radial functions…
Realizing data features by deep nets
Zheng-Chu Guo, Lei Shi, Shao-Bo Lin
This paper considers the power of deep neural networks (deep nets for short) in realizing data features. Based on refined covering number estimates, we find that, to realize some c…
Generalization and Expressivity for Deep Nets
Shao-Bo Lin
Along with the rapid development of deep learning in practice, the theoretical explanations for its success become urgent. Generalization and expressivity are two widely used measu…