Summary of Dagnosis: Localized Identification Of Data Inconsistencies Using Structures, by Nicolas Huynh et al.
DAGnosis: Localized Identification of Data Inconsistencies using Structures
by Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Machine learning models require reliable data to function effectively, and recent methods have improved identification of inconsistencies. However, these approaches still have limitations: they may not work well when features are independent, and they can’t pinpoint why a sample is inconsistent. To overcome these issues, we propose DAGnosis, which uses directed acyclic graphs (DAGs) to encode the probability distribution of features and their independencies as a structure. This allows us to localize the causes of inconsistencies on a graph and provides more accurate conclusions in detecting inconsistencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models need reliable data to work well. Some recent methods can find problems with the data, but they have two big limitations. First, they might not do so well when certain features are independent. Second, they can’t figure out why a particular piece of data is problematic. To fix these issues, researchers created DAGnosis, which uses special graphs to understand how different features are related and how they affect the data. This helps identify problems with the data more accurately and provides detailed information about what’s going wrong. |
Keywords
* Artificial intelligence * Machine learning * Probability
