Summary of Sparse Linear Regression and Lattice Problems, by Aparna Gupte et al.
Sparse Linear Regression and Lattice Problems
by Aparna Gupte, Neekon Vafa, Vinod Vaikuntanathan
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The paper investigates sparse linear regression (SLR), a statistical problem where a design matrix and response vector are given. The goal is to find a sparse solution that minimizes the mean squared prediction error. While existing methods like basis pursuit, Lasso, and the Dantzig selector work well for well-conditioned matrices, there is no known algorithm or evidence of hardness in an average-case setting. The authors aim to bridge this gap by developing new algorithms and analyzing their performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a way to find the best answer when you have some information about how things relate (design matrix) and some data points that show what happened (response vector). You want to find a short list of important things (sparse solution) that helps predict what will happen in the future. Some methods work well if the information about relationships is good, but nobody knows a way that always works or proves it’s hard for computers to figure out. |
Keywords
* Artificial intelligence * Linear regression
