Summary of Computable Learning Of Natural Hypothesis Classes, by Matthew Harrison-trainor et al.
Computable learning of natural hypothesis classes
by Matthew Harrison-Trainor, Syed Akbari
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Logic (math.LO)
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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 a new concept in machine learning called computably probably approximately correct (CPAC) learning, which sits between statistical learning theory and efficient PAC learning. The authors identify limitations in current approaches to CPAC learning, particularly regarding the learnability of certain hypothesis classes. To address these issues, they leverage tools from computability theory to show that under specific conditions, any natural hypothesis class that is learnable must also be computably learnable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to study how computers can learn from data. It’s called computably probably approximately correct learning (CPAC). The authors want to understand when this kind of learning works and when it doesn’t. They found some examples that don’t fit well with what we currently know, but these examples are a bit strange. To fix this, they use special tools to show that if something is learnable in a natural way, then it should also be computably learnable. |
Keywords
* Artificial intelligence * Machine learning
