Summary of Agnostic Learning Of Mixed Linear Regressions with Em and Am Algorithms, by Avishek Ghosh and Arya Mazumdar
Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms
by Avishek Ghosh, Arya Mazumdar
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (cs.LG)
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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 A machine learning study investigates mixed linear regression models, aiming to identify a set of linear relationships that best fit given samples of covariates and labels. Typically, the label is generated stochastically by selecting one of multiple linear functions, applying it to the covariates, and introducing noise. The goal is to estimate the underlying linear functions with some error tolerance. The paper analyzes popular algorithms like expectation maximization (EM) and alternating minimization (AM), which have been previously studied for this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mixed linear regression is a statistical problem that tries to find the best fit between samples of covariates and labels. It’s like trying to guess what formula was used to create the data, but with some noise added in. The goal is to get close to the correct formulas. Some popular methods for doing this are EM and AM. |
Keywords
» Artificial intelligence » Linear regression » Machine learning
