# Sparse Quantile Huber Regression for Efficient and Robust Estimation

arXiv: https://arxiv.org/abs/1402.4624 · HTML: https://mlsanity.com/paper/1402.4624/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2014.

## Built on by (most cited first, 2 of 2)

- [Normality-Guided Distributional Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/2208.13125/) (2022)
- [Byzantine-Robust Distributed Sparse Learning Revisited](https://mlsanity.com/paper/2605.13283/) (2026)

## Abstract (arXiv)

We consider new formulations and methods for sparse quantile regression in the high-dimensional setting. Quantile regression plays an important role in many applications, including outlier-robust exploratory analysis in gene selection. In addition, the sparsity consideration in quantile regression enables the exploration of the entire conditional distribution of the response variable given the predictors and therefore yields a more comprehensive view of the important predictors. We propose a generalized OMP algorithm for variable selection, taking the misfit loss to be either the traditional quantile loss or a smooth version we call quantile Huber, and compare the resulting greedy approaches with convex sparsity-regularized formulations. We apply a recently proposed interior point methodology to efficiently solve all convex formulations as well as convex subproblems in the generalized OMP setting, pro- vide theoretical guarantees of consistent estimation, and demonstrate the performance of our approach using empirical studies of simulated and genomic datasets.