Summary of Adatrans: Feature-wise and Sample-wise Adaptive Transfer Learning For High-dimensional Regression, by Zelin He et al.
AdaTrans: Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Regression
by Zelin He, Ying Sun, Jingyuan Liu, Runze Li
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
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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 adaptive transfer learning methods for high-dimensional linear regression problems where the feature dimension exceeds the sample size. It introduces F-AdaTrans, which detects and aggregates feature-wise transferable structures by employing a fused-penalty and adapting weights, as well as S-AdaTrans, which optimizes information transferred from each source sample using theoretically informed data-driven procedures. The methods are shown to achieve competitive performance with existing approaches in simulation and real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers develop new techniques for learning transferable patterns in high-dimensional linear regression problems. They create two different algorithms, F-AdaTrans and S-AdaTrans, that can find important features or signals in the data. The methods are tested on both made-up and real data and perform well compared to other approaches. |
Keywords
* Artificial intelligence * Linear regression * Transfer learning
