Summary of Scalable Out-of-distribution Robustness in the Presence Of Unobserved Confounders, by Parjanya Prashant et al.
Scalable Out-of-distribution Robustness in the Presence of Unobserved Confounders
by Parjanya Prashant, Seyedeh Baharan Khatami, Bruno Ribeiro, Babak Salimi
First submitted to arxiv on: 29 Nov 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 The paper explores the challenge of out-of-distribution (OOD) generalization, where unobserved confounders affect both input features and labels. It highlights the limitations of traditional domain adaptation methods when faced with unseen data distributions and proposes a simpler predictor that outperforms existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers tackle the problem of making accurate predictions when the underlying distribution is unknown. They discuss how current methods rely on multiple additional variables, which can be complex and difficult to implement. Instead, they propose a new approach that simplifies the prediction process while still achieving better results. |
Keywords
» Artificial intelligence » Domain adaptation » Generalization
