Summary of List Sample Compression and Uniform Convergence, by Steve Hanneke et al.
List Sample Compression and Uniform Convergence
by Steve Hanneke, Shay Moran, Tom Waknine
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper explores classical principles related to generalization in supervised classification known as list learning. List learning involves outputting multiple plausible labels for each instance, rather than just one. The authors investigate whether classical principles like uniform convergence and sample compression retain their applicability in the domain of list PAC learning. In traditional PAC learning, these principles satisfy a form of “completeness,” meaning that whenever a class is learnable, it can be learned by a rule that adheres to these principles. The researchers ask if this same completeness holds true in the context of list learning. The paper aims to determine whether classical principles related to generalization remain applicable in the list learning setting. By examining uniform convergence and sample compression, the authors hope to shed light on the nature of learnability in this domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary List learning is a type of supervised classification where the computer tries to find many correct answers for each problem rather than just one. This paper looks at old ideas about how well computers generalize from small samples to large datasets. The researchers want to know if these old ideas still work when using list learning instead of regular classification. They’re trying to figure out if it’s possible to learn new things by following certain rules, like making sure the computer finds the most common answer. They’re asking if these rules are complete, meaning that if something can be learned, there will always be a way for the computer to do it. The paper is important because it helps us understand how computers can learn from small samples and make good predictions about new data. |
Keywords
* Artificial intelligence * Classification * Generalization * Supervised
