Summary of A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression, by Tin Sum Cheng and Aurelien Lucchi and Anastasis Kratsios and David Belius
A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression
by Tin Sum Cheng, Aurelien Lucchi, Anastasis Kratsios, David Belius
First submitted to arxiv on: 23 Oct 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 This comprehensive study on kernel ridge regression (KRR) learning curves under minimal assumptions contributes threefold: analyzing key kernel properties, demonstrating the Gaussian Equivalent Property (GEP), and deriving novel bounds that improve existing ones across various settings. The analysis highlights the role of spectral eigen-decay, eigenfunctions’ characteristics, and kernel smoothness in KRR’s generalization performance. By replacing whitened features with standard Gaussian vectors, the GEP validates previous analyzes under the Gaussian Design Assumption, shedding light on KRR’s success. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how kernel ridge regression (KRR) works and how well it performs when given limited information. The researchers looked at three important aspects of KRR: what makes its “kernel” work, a rule called the Gaussian Equivalent Property that helps explain why KRR is good, and new rules that help predict KRR’s performance in different situations. |
Keywords
» Artificial intelligence » Generalization » Regression
