Learning rates for the risk of kernel-based quantile regression estimators in additive models

DOI10.1142/S0219530515500050zbMath1338.62077arXiv1405.3379MaRDI QIDQ2805231

Ding-Xuan Zhou, Andreas Christmann

Publication date: 10 May 2016

Published in: Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1405.3379

zbMATH Keywords

rate of convergencequantile regressionsupport vector machineadditive model

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Computational learning theory (68Q32) Nonparametric estimation (62G05)

Related Items (14)

On the robustness of regularized pairwise learning methods based on kernels ⋮ HARFE: hard-ridge random feature expansion ⋮ Learning rates for the kernel regularized regression with a differentiable strongly convex loss ⋮ On the K-functional in learning theory ⋮ A new large-scale learning algorithm for generalized additive models ⋮ Error analysis for coefficient-based regularized regression in additive models ⋮ Asymptotic analysis for affine point processes with large initial intensity ⋮ Communication-efficient estimation of high-dimensional quantile regression ⋮ Oracle inequalities for sparse additive quantile regression in reproducing kernel Hilbert space ⋮ Generalized support vector regression: Duality and tensor-kernel representation ⋮ Theory of deep convolutional neural networks. II: Spherical analysis ⋮ Sparse additive machine with ramp loss ⋮ Optimal learning with Gaussians and correntropy loss ⋮ Robust wavelet-based estimation for varying coefficient dynamic models under long-dependent structures

Uses Software

• hgam

Cites Work

• On consistency and robustness properties of support vector machines for heavy-tailed distributions
• Generalized additive models
• Support-vector networks
• Shannon sampling. II: Connections to learning theory
• ONLINE REGRESSION WITH VARYING GAUSSIANS AND NON-IDENTICAL DISTRIBUTIONS
• Learning Theory
• Minimax-optimal rates for sparse additive models over kernel classes via convex programming
• Scattered Data Approximation
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