Summary of Heterogeneous Transfer Learning For High Dimensional Regression with Feature Mismatch, by Jae Ho Chang et al.
Heterogeneous transfer learning for high dimensional regression with feature mismatch
by Jae Ho Chang, Massimiliano Russo, Subhadeep Paul
First submitted to arxiv on: 24 Dec 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 proposed two-stage method for heterogeneous transfer learning in high-dimensional regression models with possibly different features enables statistical error guarantees, addressing a key limitation of existing methods. By first learning the relationship between missing and observed features through a projection step in the proxy data, and then solving a joint penalized regression optimization problem in the target data, this approach can handle scenarios where the target and proxy feature spaces are inherently different. The method’s performance is analyzed through upper bounds on parameter estimation risk and prediction risk, revealing how these errors depend on model complexity, sample size, overlap extent, and correlation between matched and mismatched features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about transferring knowledge from one place to another when we have a lot of information in the first place but not as much in the second. Imagine you want to learn something new, like how to fix a bike, but there’s not enough data available where you are trying to learn it. Instead, you look at similar problems that people had elsewhere and try to apply what they learned to your situation. This is called “transfer learning”. But this process can be tricky if the problems are very different from each other. The authors of this paper propose a new way of doing transfer learning that takes into account these differences and provides a guarantee on how well it will work. |
Keywords
» Artificial intelligence » Optimization » Regression » Transfer learning
