Summary of A Theory Of Interpretable Approximations, by Marco Bressan et al.
A Theory of Interpretable Approximations
by Marco Bressan, Nicolò Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen
First submitted to arxiv on: 15 Jun 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 Medium Difficulty Summary: This paper explores the concept of interpretable approximations in machine learning, specifically focusing on whether a deep neural network can be approximated by a small decision tree using simple features. The authors introduce the notion of interpretable approximations, which involves approximating a target concept by aggregating concepts from a base class. They demonstrate a remarkable trichotomy, where for any given pair of base class and target concept, exactly one of three cases holds: the concept cannot be approximated, it can be approximated but with no universal rate bounding complexity, or it can be approximated with a constant complexity bound depending only on the base class and concept. The authors also extend this trichotomy to classes with unbounded VC dimension and provide characterizations of interpretability based on algebra generated by the base class. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This research paper is about making machine learning models more understandable for humans. It’s like trying to find a simple way to describe a complex idea using just small building blocks. The authors discovered that there are only three ways to do this: either it can’t be done, or it can be done but with no clear limit on how complex the answer is, or it can be done with a guarantee that the answer will be simple and easy to understand. This has important implications for how we use machine learning in real-life applications. |
Keywords
* Artificial intelligence * Decision tree * Machine learning * Neural network
