JMLR

Outlier Robust and Sparse Estimation of Linear Regression Coefficients

Authors
Takeyuki Sasai Hironori Fujisawa
Research Topics
Machine Learning High-Dimensional Statistics
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Paper Information
  • Journal:
    Journal of Machine Learning Research
  • Added to Tracker:
    Jul 30, 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
Author
Hironori Fujisawa
Author
Research Topics & Keywords
Machine Learning
Research Area
High-Dimensional Statistics
Research Area
Citation Information
APA Format
Takeyuki Sasai & Hironori Fujisawa . Outlier Robust and Sparse Estimation of Linear Regression Coefficients. Journal of Machine Learning Research .
BibTeX Format
@article{paper152,
  title = { Outlier Robust and Sparse Estimation of Linear Regression Coefficients },
  author = { Takeyuki Sasai and Hironori Fujisawa },
  journal = { Journal of Machine Learning Research },
  url = { https://www.jmlr.org/papers/v26/23-1583.html }
}
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