Summary of Transfusion: Covariate-shift Robust Transfer Learning For High-dimensional Regression, by Zelin He et al.
TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regression
by Zelin He, Ying Sun, Jingyuan Liu, Runze Li
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
GrooveSquid.com Paper Summaries
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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 proposed two-step method with a novel fused-regularizer tackles model shifts in high-dimensional regression settings by leveraging samples from source tasks to improve learning performance on target tasks with limited samples. The approach provides a nonasymptotic bound for estimation error, demonstrating robustness to covariate shifts and establishing conditions for minimax-optimal estimators. The method is further extended to distributed settings, allowing for pretraining-finetuning strategies that retain the centralized version’s estimation rate while requiring only one round of communication. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to improve learning performance on target tasks with limited samples by using information from source tasks. It works in high-dimensional regression settings where there are big differences between the distributions of features and target variables. The approach provides guarantees about how well it will work, even when there are large shifts between the source and target data. It also shows how to apply this method in distributed computing environments. |
Keywords
* Artificial intelligence * Pretraining * Regression
