Summary of Targeted Synthetic Data Generation For Tabular Data Via Hardness Characterization, by Tommaso Ferracci et al.
Targeted synthetic data generation for tabular data via hardness characterization
by Tommaso Ferracci, Leonie Tabea Goldmann, Anton Hinel, Francesco Sanna Passino
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A novel data augmentation approach has been proposed, leveraging synthetic data generation to enhance model performance and robustness when working with scarce or low-quality data. The method, which combines a data valuation framework with hardness characterization, generates high-value training points in a computationally efficient manner. Experimental results on real-world benchmarks demonstrate that Shapley-based data valuation methods rival learning-based approaches in characterizing task hardness while offering significant computational advantages. Moreover, synthetic data generators trained on the hardest points outperform non-targeted augmentation on various tabular datasets, leading to improved out-of-sample prediction quality and reduced computational complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of improving machine learning models has been found! By creating fake training data that’s very good at helping the model learn, we can make it work better even when we don’t have a lot of real data. This is especially useful when the data we do have is low-quality or tricky to use. The method works by finding the most important parts of the data and using those to generate new training examples that are really helpful. Tests show that this approach can make models work better, even on harder problems. |
Keywords
» Artificial intelligence » Data augmentation » Machine learning » Synthetic data
