Summary of Fully Test-time Adaptation For Tabular Data, by Zhi Zhou et al.
Fully Test-time Adaptation for Tabular Data
by Zhi Zhou, Kun-Yang Yu, Lan-Zhe Guo, Yu-Feng Li
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed Fully Test-time Adaptation for Tabular data (FTAT) addresses the challenge of adapting tabular models to generalize to unknown distributions during testing, enabling robust optimization of label distribution and adaptation to shifted covariate distributions. By leveraging three key techniques – optimizing label distribution, adapting to shifted covariates, and suitably evaluating tasks and models – FTAT outperforms state-of-the-art methods on six benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tabular data is important in many real-world situations. Researchers have developed deep tabular models that work well, but they struggle when the testing data is different from what was used to train them. To solve this problem, scientists created a new way to adapt these models using only the testing data. This approach helps the model learn and improve as it deals with unknown distributions during testing. The proposed method, FTAT, uses three key ideas: optimizing label distribution, adapting to shifted covariates, and evaluating tasks and models. By doing this, FTAT outperforms other methods on six datasets. |
Keywords
» Artificial intelligence » Optimization
