Summary of Lepskii Principle For Distributed Kernel Ridge Regression, by Shao-bo Lin
Lepskii Principle for Distributed Kernel Ridge Regression
by Shao-Bo Lin
First submitted to arxiv on: 8 Sep 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 The proposed adaptive distributed kernel ridge regression (Lep-AdaDKRR) model addresses the challenge of parameter selection in distributed learning without communicating local data. Building on the Lepskii principle and non-privacy communication protocol for kernel learning, the authors develop a Lepskii-based approach to equip DKRR and adaptively adjust its parameters. Theoretical analysis shows that Lep-AdaDKRR successfully adapts to regression function regularity, kernel dimensionality, and generalization metrics, bridging the gap between theoretical analysis and practical application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way for machines to learn from distributed data without sharing information. They combined two existing ideas: the Lepskii principle and non-privacy communication protocol. This new approach helps machines adapt to different types of data and tasks. The authors showed that this method works well in theory, which is important because it means other researchers can build upon their work. |
Keywords
» Artificial intelligence » Generalization » Regression
