Summary of A Non-asymptotic Theory Of Kernel Ridge Regression: Deterministic Equivalents, Test Error, and Gcv Estimator, by Theodor Misiakiewicz et al.
A non-asymptotic theory of Kernel Ridge Regression: deterministic equivalents, test error, and GCV estimator
by Theodor Misiakiewicz, Basil Saeed
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 paper explores learning an unknown target function using kernel ridge regression (KRR) given i.i.d. data. It examines the relationship between KRR and an equivalent sequence model that only depends on the spectrum of the kernel operator, which has been empirically shown to approximate the test error of KRR. The authors aim to provide a theoretical justification for this equivalence, building upon recent work in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a machine learning technique called kernel ridge regression (KRR) to learn an unknown function from some data. They want to understand why this method works well and how it compares to other ways of doing things. The idea is that KRR can be related to another model, the sequence model, which helps us predict how well KRR will do on new, unseen data. |
Keywords
* Artificial intelligence * Machine learning * Regression * Sequence model
