Summary of Robust and Sparse Estimation Of Linear Regression Coefficients with Heavy-tailed Noises and Covariates, by Takeyuki Sasai
Robust and Sparse Estimation of Linear Regression Coefficients with Heavy-tailed Noises and Covariates
by Takeyuki Sasai
First submitted to arxiv on: 15 Jun 2022
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a method for estimating linear regression coefficients in situations where data is contaminated with malicious outliers from heavy-tailed distributions. The proposed estimator is efficient to compute and provides nearly optimal error bounds. The approach tackles challenges posed by sparse and robust estimation, which is critical when dealing with noisy or corrupted data. By leveraging techniques from statistical learning theory, the authors demonstrate improved performance on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores a way to accurately estimate the relationships between variables even when some of the data is fake or very unusual. The approach helps fix problems that can occur when there’s noise or mistakes in the data. It works well and provides good results, making it useful for situations where we don’t have perfect information. |
Keywords
* Artificial intelligence * Linear regression
