Summary of Statistical Inference For Feature Selection After Optimal Transport-based Domain Adaptation, by Nguyen Thang Loi et al.
Statistical Inference for Feature Selection after Optimal Transport-based Domain Adaptation
by Nguyen Thang Loi, Duong Tan Loc, Vo Nguyen Le Duy
First submitted to arxiv on: 19 Oct 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 proposes a novel statistical method called SFS-DA (statistical Feature Selection under Domain Adaptation) to guarantee the reliability of feature selection under domain adaptation. The method leverages the Selective Inference framework and carefully examines the feature selection process under domain adaptation, which can be characterized by linear and quadratic inequalities. This approach enables control over the false positive rate while maximizing the true positive rate. Experimental results on synthetic and real-world datasets support the theoretical findings and demonstrate the superior performance of SFS-DA compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way to make sure that when we choose important features from data that doesn’t belong to our main topic (domain adaptation), our choice is reliable. They call this method SFS-DA. This approach helps us control the number of mistakes we might make while still finding most of the correct important features. The team tested their method on fake and real-world datasets and found that it works better than other methods. |
Keywords
» Artificial intelligence » Domain adaptation » Feature selection » Inference
