Adversarial robust weighted Huber regression
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 15 references that are in the map. First posted on arXiv 22 Feb 2021.
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References · 7
- 2018Efficient Algorithms and Lower Bounds for Robust Linear Regressioncited by 76
- 2018Efficient Algorithms for Outlier-Robust Regressioncited by 72
- 2020Robust Linear Regression: Optimal Rates in Polynomial Timecited by 42
- 2019Outlier-robust estimation of a sparse linear model using ℓ1-penalized Huber's M-estimatorcited by 31
- 2020Optimal Robust Linear Regression in Nearly Linear Timecited by 31
- 2019Robust Inference via Multiplier Bootstrapcited by 8
- 2020Outlier-robust sparse/low-rank least-squares regression and robust matrix completioncited by 6
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