Summary of Optimal Kernel Quantile Learning with Random Features, by Caixing Wang and Xingdong Feng
Optimal Kernel Quantile Learning with Random Features
by Caixing Wang, Xingdong Feng
First submitted to arxiv on: 24 Aug 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 The paper presents a generalization of kernel quantile regression with random features (KQR-RF), which addresses limitations of existing kernel ridge regression with random features (KRR-RF) in handling heterogeneous data with heavy-tailed noises. The authors introduce a refined error decomposition and establish a connection between KQR-RF and KRR-RF, deriving capacity-dependent learning rates for KQR-RF under mild conditions on the number of random features. These results can be extended to agnostic settings where the target quantile function may not align with the assumed kernel space, and also applied to cases with Lipschitz continuous losses. Theoretical findings are validated through simulated experiments and a real data application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes machine learning more efficient by improving kernel quantile regression with random features (KQR-RF). Right now, KRR-RF is the most common way to do this, but it has some big limitations when dealing with noisy or uneven data. The authors come up with a new approach that fixes these problems and shows how their method can be used in lots of different situations. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Regression
