Summary of Sparse Linear Regression When Noises and Covariates Are Heavy-tailed and Contaminated by Outliers, By Takeyuki Sasai and Hironori Fujisawa
Sparse Linear Regression when Noises and Covariates are Heavy-Tailed and Contaminated by Outliers
by Takeyuki Sasai, Hironori Fujisawa
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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| 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 paper proposes new methods for estimating coefficients in linear regression when dealing with heavy-tailed data and outliers. The authors assume that the covariates and noise variables are sampled from heavy-tailed distributions and that some of these samples may be contaminated by outliers. The proposed estimators can be computed efficiently and provide sharp error bounds, making them useful for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out how to do a better job estimating things in linear regression when the data is weirdly distributed or has some bad points mixed in. They’re talking about situations where the data doesn’t follow normal rules, like having lots of extreme values or being messy with outliers. The cool thing is that their new way of doing this can be done quickly and gives us a good idea of how accurate it will be. |
Keywords
* Artificial intelligence * Linear regression
