19 citations · 37 across the 15 of their papers we have counts for
15 papers
Generalization Analysis of Distributed Kernel-based Robust Gradient Descent Algorithms
Jun-Yi Meng, Zheng-Chu Guo, Yuan Mao
In this paper, we investigate the generalization performance of distributed gradient descent algorithms in a reproducing kernel Hilbert space under a robust loss function . By…
Spectral Algorithms under Covariate Shift
Jun Fan, Zheng-Chu Guo, Lei Shi
Spectral algorithms leverage spectral regularization techniques to analyze and process data, providing a flexible framework for addressing supervised learning problems. To deepen o…
Generalization Analysis of Online Stochastic Gradient Descent for Overparameterized Two-Layer Neural Networks
Dinghao Cao, Zheng-Chu Guo, Lei Shi
We investigate the generalization performance of online stochastic gradient descent (SGD) for overparameterized two-layer neural networks under the Neural Tangent Kernel (NTK) regi…
Optimality of Robust Online Learning
Zheng-Chu Guo, Andreas Christmann, Lei Shi
In this paper, we study an online learning algorithm with a robust loss function for regression over a reproducing kernel Hilbert space (RKHS). The loss function $\…
Online Regularized Learning Algorithm for Functional Data
Yuan Mao, Zheng-Chu Guo
In recent years, functional linear models have attracted growing attention in statistics and machine learning, with the aim of recovering the slope function or its functional predi…
Capacity dependent analysis for functional online learning algorithms
Xin Guo, Zheng-Chu Guo, Lei Shi
This article provides convergence analysis of online stochastic gradient descent algorithms for functional linear models. Adopting the characterizations of the slope function regul…