# Learning Rates for Kernel-Based Expectile Regression

arXiv: https://arxiv.org/abs/1702.07552 · HTML: https://mlsanity.com/paper/1702.07552/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2017.

## Built on by (most cited first, 4 of 4)

- [Total Stability of SVMs and Localized SVMs](https://mlsanity.com/paper/2101.12678/) (2021)
- [On the Connection between Lp and Risk Consistency and its Implications on Regularized Kernel Methods](https://mlsanity.com/paper/2303.15210/) (2023)
- [Self-Regularized Learning Methods](https://mlsanity.com/paper/2603.17160/) (2026)
- [Generalized nonparametric regression in reproducing kernel Hilbert spaces: Consistency and rates of convergence](https://mlsanity.com/paper/2606.22993/) (2026)

## Abstract (arXiv)

Conditional expectiles are becoming an increasingly important tool in finance as well as in other areas of applications. We analyse a support vector machine type approach for estimating conditional expectiles and establish learning rates that are minimax optimal modulo a logarithmic factor if Gaussian RBF kernels are used and the desired expectile is smooth in a Besov sense. As a special case, our learning rates improve the best known rates for kernel-based least squares regression in this scenario. Key ingredients of our statistical analysis are a general calibration inequality for the asymmetric least squares loss, a corresponding variance bound as well as an improved entropy number bound for Gaussian RBF kernels.