Summary of Learning Tree Pattern Transformations, by Daniel Neider and Leif Sabellek and Johannes Schmidt and Fabian Vehlken and Thomas Zeume
Learning Tree Pattern Transformations
by Daniel Neider, Leif Sabellek, Johannes Schmidt, Fabian Vehlken, Thomas Zeume
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Databases (cs.DB)
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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 explores how to learn explanations for structural differences between pairs of trees from sample data. The goal is to find a small set of rules that explains the differences between all pairs of labelled, ordered trees. Two research questions are addressed: what is a good notion of “rule” in this context? and how can sets of rules explaining a dataset be learned algorithmically? |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand why some XML or JSON data looks different from others. It wants to find simple rules that explain these differences. The researchers ask two big questions: what does it mean for a rule to “explain” the difference? and how can we use computers to learn these rules from example data? |
