Summary of Near-interpolators: Rapid Norm Growth and the Trade-off Between Interpolation and Generalization, by Yutong Wang et al.
Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization
by Yutong Wang, Rishi Sonthalia, Wei Hu
First submitted to arxiv on: 12 Mar 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 studies the generalization capabilities of nearly-interpolating linear regressors, which are models with training errors below the noise floor. Under certain assumptions on the data distribution and covariance matrix, it is shown that these models exhibit rapid norm growth, contradicting existing data-independent bounds. This implies that larger models are not necessarily better at generalizing. The paper also precisely characterizes the trade-off between interpolation and generalization for these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study explores how machine learning models behave when they’re really good at fitting the training data but not perfect. It shows that even small mistakes in fitting can lead to big problems with how well the model works on new, unseen data. This is important because we want our models to be able to learn from new data and make good decisions. The researchers found that if a model is really good at fitting the training data, it’s likely not going to do as well when faced with new information. |
Keywords
* Artificial intelligence * Generalization * Machine learning
