Summary of Generalising Realisability in Statistical Learning Theory Under Epistemic Uncertainty, by Fabio Cuzzolin
Generalising realisability in statistical learning theory under epistemic uncertainty
by Fabio Cuzzolin
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Statistics Theory (math.ST)
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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 The proposed paper explores how fundamental concepts in statistical learning theory, including realizability, generalize when training and testing datasets are drawn from the same credal set – a convex set of probability distributions. This work aims to lay the groundwork for a broader treatment of statistical learning under epistemic uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a machine to learn from data. But what if the machine doesn’t know which type of data it’s working with? This paper looks at how we can make sure the machine generalizes well even when it’s not clear what kind of data it’s seeing. It’s a big step towards making machines that can learn and adapt in uncertain situations. |
Keywords
* Artificial intelligence * Probability
