Summary of Spectrally Transformed Kernel Regression, by Runtian Zhai et al.
Spectrally Transformed Kernel Regression
by Runtian Zhai, Rattana Pukdee, Roger Jin, Maria-Florina Balcan, Pradeep Ravikumar
First submitted to arxiv on: 1 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 paper explores the role of unlabeled data in machine learning, specifically in spectral transformed kernel regression (STKR). The authors revisit the classical STKR idea and develop a new class of general and scalable STKR estimators that can leverage unlabeled data. They demonstrate that STKR is a principled approach by characterizing a universal type of “target smoothness” and proving that any sufficiently smooth function can be learned by STKR. The paper also provides scalable implementations for the inductive setting and statistical guarantees for two scenarios: STKR with a known polynomial transformation, and STKR with kernel PCA when the transformation is unknown. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Unlabeled data helps machine learning work better. This research looks at how to use this kind of data to make predictions. The idea is to take some labeled data (where we know what’s correct) and add some unlabeled data to it. This can help the computer learn more about the patterns in the data. The authors show that their method, called spectral transformed kernel regression (STKR), works well and can be used for many different types of problems. |
Keywords
* Artificial intelligence * Machine learning * Pca * Regression
