# An SVM-like Approach for Expectile Regression

arXiv: https://arxiv.org/abs/1507.03887 · HTML: https://mlsanity.com/paper/1507.03887/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2015.

## Built on by (most cited first, 3 of 3)

- [A review of predictive uncertainty estimation with machine learning](https://mlsanity.com/paper/2209.08307/) (2022)
- [Neural Networks for Censored Expectile Regression Based on Data Augmentation](https://mlsanity.com/paper/2510.20344/) (2025)
- [Self-Regularized Learning Methods](https://mlsanity.com/paper/2603.17160/) (2026)

## Abstract (arXiv)

Expectile regression is a nice tool for investigating conditional distributions beyond the conditional mean. It is well-known that expectiles can be described with the help of the asymmetric least square loss function, and this link makes it possible to estimate expectiles in a non-parametric framework by a support vector machine like approach. In this work we develop an efficient sequential-minimal-optimization-based solver for the underlying optimization problem. The behavior of the solver is investigated by conducting various experiments and the results are compared with the recent R-package ER-Boost.