Summary of Benign Overfitting in Out-of-distribution Generalization Of Linear Models, by Shange Tang et al.
Benign Overfitting in Out-of-Distribution Generalization of Linear Models
by Shange Tang, Jiayun Wu, Jianqing Fan, Chi Jin
First submitted to arxiv on: 19 Dec 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 delves into benign overfitting in modern machine learning, a phenomenon where an over-parameterized model fits training data perfectly but still generalizes well to unseen test data. The authors focus on linear models under covariate shift, providing non-asymptotic guarantees for standard ridge regression and identifying vital quantities governing OOD generalization. Their results recover prior benign overfitting guarantees in the in-distribution setup and under-parameterized OOD setup. Moreover, they show that Principal Component Regression (PCR) achieves a faster rate of convergence than standard ridge regression. The authors’ findings shed light on the intricate relationship between model complexity and out-of-distribution generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how big models can still work well even when they’re perfect at fitting training data. This might seem weird, but it’s actually a good thing! The authors are trying to understand why this happens when we use different kinds of data for training and testing. They found that some special conditions need to be met for this “benign overfitting” to occur, which means that big models can still generalize well even when they’re perfect at fitting the training data. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Overfitting » Regression
