Summary of Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction, by Kulunu Dharmakeerthi et al.
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction
by Kulunu Dharmakeerthi, YoonHaeng Hur, Tengyuan Liang
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME); 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 In this paper, researchers tackle the domain adaptation problem in observational data, where a prediction model trained on one environment may not generalize well to another environment with shifted covariates and responses. The authors focus on understanding how confounding factors can cause concept shifts and covariate shifts, making it challenging for models to accurately predict target responses. They propose a new representation learning method that optimizes for a lower-dimensional linear subspace, which is more robust to both concept and covariate shifts. This approach involves a non-convex objective function constrained on the Stiefel manifold, allowing the model to balance predictability and stability. The authors demonstrate their method’s effectiveness on three real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are trying to help machines learn from one environment and apply that learning to another environment. They’re looking at how things can change between environments, like when a cat is trained to recognize dogs in one place but then sees different breeds of dogs elsewhere. The authors have come up with a new way to teach machines to adapt to these changes by finding the most important features that don’t change much between environments. This helps the machine make better predictions about things it hasn’t seen before. |
Keywords
* Artificial intelligence * Domain adaptation * Objective function * Representation learning
