Summary of On the Minimal Degree Bias in Generalization on the Unseen For Non-boolean Functions, by Denys Pushkin et al.
On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions
by Denys Pushkin, Raphaël Berthier, Emmanuel Abbe
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper investigates the out-of-domain generalization capabilities of random feature (RF) models and Transformers in various regimes. In the “generalization on the unseen” (GOTU) setting, RF models with small feature regimes converge to interpolators of minimal degree, similar to Boolean cases. The sparse target regime is also explored, showing different outcomes depending on data embedding methods. For q-ary data tokens embedded with roots of unities, a min-degree interpolator is learned, while non-embedded integers or reals may not lead to this outcome. This study highlights the special case of Boolean settings and their generalization, offering insights into learning mechanisms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machine learning models like RF and Transformers perform when used with new data they haven’t seen before. It finds that in some cases, these models can learn simple patterns and predict well, but this isn’t always true. The study shows that the way the data is represented matters a lot. If the data is set up in a specific way, the model will try to find the simplest solution possible, which is useful for making predictions. However, if the data is presented differently, the model might not be able to find this simple solution and may not work as well. |
Keywords
» Artificial intelligence » Domain generalization » Embedding » Generalization » Machine learning
