Summary of Retasa: a Nonparametric Functional Estimation Approach For Addressing Continuous Target Shift, by Hwanwoo Kim et al.
ReTaSA: A Nonparametric Functional Estimation Approach for Addressing Continuous Target Shift
by Hwanwoo Kim, Xin Zhang, Jiwei Zhao, Qinglong Tian
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 tackles the challenge of deploying machine learning models in real-world applications where there are distribution shifts between training and testing data. Specifically, it focuses on the target shift problem in regression settings, where the marginal distributions of the continuous response variable differ between domains, but the conditional distribution of features given the response remains the same. The authors show that existing methods for classification tasks with finite target spaces don’t apply to this regression problem with an infinite-dimensional target space. Instead, they propose a nonparametric regularized approach called ReTaSA to estimate importance weights from an ill-posed integral equation and provide theoretical justification for their method. The effectiveness of ReTaSA is demonstrated through extensive numerical studies on synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in machine learning! Imagine you trained a model to predict something, but when you use it in the real world, the numbers are different. That’s called a “target shift” problem. The authors of this paper figured out how to fix this problem by creating a new way to estimate “importance weights”. These importance weights help the model understand that some data points are more important than others. They tested their method on pretend and real data, and it worked really well! |
Keywords
* Artificial intelligence * Classification * Machine learning * Regression
