Summary of Optimal Transport For Structure Learning Under Missing Data, by Vy Vo et al.
Optimal Transport for Structure Learning Under Missing Data
by Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 tackles the problem of learning causal structures from missing data, which is a common issue in many fields such as medicine and social sciences. The authors propose a novel algorithm that uses optimal transport theory to learn the underlying causal structure from incomplete data. Unlike traditional imputation methods, this approach directly considers the dependencies or causal relations among variables when filling in missing values. As a result, it can recover the true causal graph more effectively than existing methods and even surpass them in scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about learning how things cause each other using incomplete information. Imagine trying to figure out what causes someone’s illness when some of their health data is missing. The usual way to deal with this problem is to fill in the gaps first, then try to understand the underlying causes. But that doesn’t always work well. This paper introduces a new approach that looks at how all the variables are related and uses that information to fill in the missing values. It’s shown to be more effective than other methods and can handle large amounts of data. |
