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Christmann, A. and Zhou, D.X. (2016) Learning Rates for the Risk of Kernel-Based Quantile Regression Estimators in Additive Models. Analysis and Applications, 14, 449-477.
https://doi.org/10.1142/S0219530515500050

has been cited by the following article:

• TITLE: Error Analysis and Variable Selection for Differential Private Learning Algorithm

  AUTHORS: Weilin Nie, Cheng Wang

  KEYWORDS: Differential Privacy, Least Squares Regularization, Concentration Inequality, Error Decomposition

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.5 No.4, April 30, 2017

  ABSTRACT: In this paper, we construct a modified least squares regression algorithm which can provide privacy protection. A new concentration inequality is applied and the expected error bound is derived by error decomposition. Furthermore, via the error analysis, we find a method to choose an appropriate parameter to balance the error and privacy.