Summary of Leveraging Pac-bayes Theory and Gibbs Distributions For Generalization Bounds with Complexity Measures, by Paul Viallard et al.
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures
by Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 research paper derives a novel general generalization bound by leveraging disintegrated PAC-Bayes bounds. The bound is instantiable with arbitrary complexity measures, enabling customization to fit specific problem settings. The proof involves considering Gibbs distributions, which allows the complexity to be adapted to the generalization gap. This work addresses limitations in traditional statistical learning theory, where generalization bounds are often tied to specific theoretical frameworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to measure how well an algorithm will perform on new data. It uses a special type of mathematical framework called disintegrated PAC-Bayes bounds. This allows the complexity (or difficulty) of the problem to be adjusted depending on what’s being learned and the task at hand. The method involves considering certain types of probability distributions, which makes it more flexible than traditional methods. |
Keywords
* Artificial intelligence * Generalization * Probability
