Summary of Learning Fair Invariant Representations Under Covariate and Correlation Shifts Simultaneously, by Dong Li et al.
Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously
by Dong Li, Chen Zhao, Minglai Shao, Wenjun Wang
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 tackles the challenging problem of achieving domain generalization while considering model fairness in machine learning. The authors introduce a novel approach that addresses both covariate shift and correlation shift simultaneously, allowing for a predictor that is fair and invariant across different domains. To achieve this, they disentangle data into content and style factors, learning fairness-aware domain-invariant representations by mitigating sensitive information. This approach outperforms state-of-the-art methods in terms of model accuracy as well as group and individual fairness on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make machine learning models fairer and better at working with new kinds of data. Right now, it’s hard to get a model that works well across different types of data while also being fair to everyone. The authors come up with a new way to do this by breaking down the data into what matters (content) and how things look (style). They then make sure the model is treating everyone fairly while still working well with new kinds of data. |
Keywords
» Artificial intelligence » Domain generalization » Machine learning
