# Adversarial robust weighted Huber regression

arXiv: https://arxiv.org/abs/2102.11120 · HTML: https://mlsanity.com/paper/2102.11120/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Efficient Algorithms and Lower Bounds for Robust Linear Regression](https://mlsanity.com/paper/1806.00040/) (2018)
- [Efficient Algorithms for Outlier-Robust Regression](https://mlsanity.com/paper/1803.03241/) (2018)
- [Robust Linear Regression: Optimal Rates in Polynomial Time](https://mlsanity.com/paper/2007.01394/) (2020)
- [Optimal Robust Linear Regression in Nearly Linear Time](https://mlsanity.com/paper/2007.08137/) (2020)
- [Outlier-robust estimation of a sparse linear model using ℓ1-penalized Huber's M-estimator](https://mlsanity.com/paper/1904.06288/) (2019)
- [Robust Inference via Multiplier Bootstrap](https://mlsanity.com/paper/1903.07208/) (2019)
- [Outlier-robust sparse/low-rank least-squares regression and robust matrix completion](https://mlsanity.com/paper/2012.06750/) (2020)

## Abstract (arXiv)

We consider a robust estimation of linear regression coefficients. In this note, we focus on the case where the covariates are sampled from an L-subGaussian distribution with unknown covariance, the noises are sampled from a distribution with a bounded absolute moment and both covariates and noises may be contaminated by an adversary. We derive an estimation error bound, which depends on the stable rank and the condition number of the covariance matrix of covariates with a polynomial computational complexity of estimation.