Summary of Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference, by Yujin Han et al.
Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference
by Yujin Han, Difan Zou
First submitted to arxiv on: 22 Apr 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 method, GIC, aims to improve group robustness in standard empirical risk minimization (ERM) models by accurately inferring group labels. This is achieved by training a spurious attribute classifier based on two key properties of spurious correlations: high correlation between spurious attributes and true labels, and variability in this correlation between datasets with different group distributions. The method demonstrates effectiveness in inferring group labels and improving worst-group accuracy when combined with various downstream invariant learning methods. Additionally, the analysis of misclassifications reveals an interesting phenomenon called semantic consistency, which may contribute to better decoupling the association between spurious attributes and labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GIC is a new way to figure out what group someone belongs to, even if you don’t have that information. This helps make machine learning models more fair by reducing their mistakes on groups where they don’t work well. The method works by training a special type of model that looks for patterns in the data that are specific to certain groups. By doing this, it can accurately guess what group someone belongs to and improve the performance of the model on that group. GIC is useful because it can be used with many different types of machine learning models and datasets. |
Keywords
» Artificial intelligence » Machine learning
