Summary of Robustness Auditing For Linear Regression: to Singularity and Beyond, by Ittai Rubinstein et al.
Robustness Auditing for Linear Regression: To Singularity and Beyond
by Ittai Rubinstein, Samuel B. Hopkins
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 The paper explores the phenomenon where influential econometrics studies’ conclusions can be reversed by removing a small fraction of their samples. It challenges the reliability of Ordinary Least Squares (OLS) regression results, particularly when considering the robustness of an OLS fit to sample removal. By certifying the robustness of an OLS fit on a given dataset, researchers can better understand the implications of this phenomenon and develop more reliable econometric models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper investigates how removing a small percentage of samples in econometrics studies can change their conclusions. It asks whether it’s possible to confirm that an OLS model is strong even if some data points are removed. The study aims to improve our understanding of OLS regression and make econometric models more trustworthy. |
Keywords
» Artificial intelligence » Regression
