Summary of Asymptotics Of Random Feature Regression Beyond the Linear Scaling Regime, by Hong Hu et al.
Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime
by Hong Hu, Yue M. Lu, Theodor Misiakiewicz
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 recent surge in machine learning advancements has been largely driven by the use of overparametrized models, which are trained until near interpolation of the training data. The double descent phenomenon, for instance, highlights that model complexity and generalization capabilities cannot be accurately measured by simply considering the number of parameters. This abstract paper aims to fill this knowledge gap by investigating how model complexity and generalization depend on the number of parameters (p) and how p should be chosen relative to the sample size (n) to achieve optimal test error. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent research has led to big breakthroughs in machine learning, thanks to using super-large models that are really good at fitting training data. However, this raises an important question: what happens when we use these huge models? How do they perform on new, unseen data? The answer lies in understanding how the size of these models affects their ability to generalize and make accurate predictions. |
Keywords
* Artificial intelligence * Generalization * Machine learning
