Summary of Minimum-norm Interpolation Under Covariate Shift, by Neil Mallinar et al.
Minimum-Norm Interpolation Under Covariate Shift
by Neil Mallinar, Austin Zane, Spencer Frei, Bin Yu
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 investigates transfer learning in high-dimensional linear regression models. Despite being extensively studied in experimental works with overparameterized neural networks, there remains a significant gap in theoretical understanding of transfer learning in simple settings like linear regression. The researchers identify a phenomenon called “benign overfitting” where linear interpolators overfit to noisy training labels but still generalize well. They then explore how high-dimensional linear models behave under transfer learning and prove non-asymptotic excess risk bounds for benignly-overfit linear interpolators. Additionally, they propose a taxonomy of beneficial and malignant covariate shifts based on the degree of overparameterization. The paper concludes with empirical studies demonstrating these beneficial and malignant shifts in real image data and fully-connected neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well machine learning models can work when they’re moved from one task to another. It’s like trying to teach a child to ride a bike by having them practice on a small, flat surface, then suddenly moving them to a big hill! The researchers found that some linear models can do surprisingly well in this situation, even if they’ve been trained on noisy data. They’re trying to understand why this happens and how it works. By studying high-dimensional linear regression models, they hope to develop new ways of making machine learning models work better when we move them from one task to another. |
Keywords
* Artificial intelligence * Linear regression * Machine learning * Overfitting * Transfer learning
