JMLR
Outlier Robust and Sparse Estimation of Linear Regression Coefficients
Authors
Takeyuki Sasai
Hironori Fujisawa
Research Topics
High-Dimensional Statistics
Machine Learning
Paper Information
-
Journal:
Journal of Machine Learning Research -
Added to Tracker:
Jul 15, 2025
Abstract
We consider outlier-robust and sparse estimation of linear regression coefficients, when the covariates and the noises are contaminated by adversarial outliers and noises are sampled from a heavy-tailed distribution. Our results present sharper error bounds under weaker assumptions than prior studies that share similar interests with this study. Our analysis relies on some sharp concentration inequalities resulting from generic chaining.
Author Details
Takeyuki Sasai
AuthorHironori Fujisawa
AuthorResearch Topics & Keywords
High-Dimensional Statistics
Research AreaMachine Learning
Research AreaCitation Information
APA Format
Takeyuki Sasai
&
Hironori Fujisawa
.
Outlier Robust and Sparse Estimation of Linear Regression Coefficients.
Journal of Machine Learning Research
.
BibTeX Format
@article{JMLR:v26:23-1583,
author = {Takeyuki Sasai and Hironori Fujisawa},
title = {Outlier Robust and Sparse Estimation of Linear Regression Coefficients},
journal = {Journal of Machine Learning Research},
year = {2025},
volume = {26},
number = {93},
pages = {1--79},
url = {http://jmlr.org/papers/v26/23-1583.html}
}