Prof. Sheng Baohuai

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Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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13 March 2024 in Analysis and Applications

Journal Article

Moduli of smoothness, K-functionals and Jackson-type inequalities associated with kernel function approximation in learning theory

Sheng Baohuai

Jian-Li Wang

21 February 2012 in Acta Mathematica Sinica, English Series

Journal Article

Learning rates for least square regressions with coefficient regularization

Sheng Baohuai

Peixin Ye

Jian Li Wang

1 January 2020 in Communications on Pure and Applied Analysis

Journal Article

Learning rates for the kernel regularized regression with a differentiable strongly convex loss

Sheng Baohuai

Huanxiang Liu

Huimin Wang

We consider learning rates of kernel regularized regression (KRR) based on reproducing kernel Hilbert spaces (RKHSs) and differentiable strongly convex losses and provide some new strongly convex losses. We first show the robustness with the maximum mean discrepancy (MMD) and the Hutchinson metric respectively, and, along this line, bound the learning rate of the KRR. We first provide a capacity dependent learning rate and then give the learning rates for four concrete strongly convex losses respectively. In particular, we provide the learning rates when the hypothesis RKHS's logarithmic complexity exponent is arbitrarily small as well as sufficiently large.

1 January 2023 in Mathematical Foundations of Computing

Journal Article

Error analysis of kernel regularized pairwise learning with a strongly convex loss

Shuhua Wang

Sheng Baohuai

This paper presents a detailed performance analysis for the kernel-based regularized pairwise learning model associated with a strongly convex loss. The robustness for the model is analyzed by applying an improved convex analysis method. The results show that the regularized pairwise learning model has better qualitatively robustness according to the probability measure. Some new comparison inequalities are provided, with which the convergence rates are derived. In particular an explicit learning rate is obtained in case that the loss is the least square loss.

23 February 2016 in International Journal of Machine Learning and Cybernetics

Journal Article

The improved learning rate for regularized regression with RKBSs

Huanxiang Liu

Sheng Baohuai

Peixin Ye

27 February 2012 in Science China Mathematics

Journal Article

The convergence rates of Shannon sampling learning algorithms

Sheng Baohuai

About Me

Sheng Baohuai attended Baoji Normal College, Baoji, Shaanxi, from 1981 to 1985. He earned his BS degree in mathematical teaching from the department of mathematics in 1985. He earned his MS degree in basic mathematics from the department of mathematics of Hangzhou University in 1988. From 1988 to 2001, he worked toward his Doctor of Sciences in applied mathematics at Xidian University, Xian, P. R. China, and earned his S.D. degree in March, 2001. From March, 2001 to September 2003, he served as a professor at the Ningbo University. Since September, 2003, he has been a faculty member of Shaoxing College of Arts and Sciences, Shaoxing, Zhejiang, P. R. China, where he serves currently as a professor. His current research interests focus on the area of approximation theory, nonlinear optimization, learning theory.

Prof. Sheng Baohuai

Prof. Sheng Baohuai

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About Me