Targeted synthetic data generation for tabular data via hardness characterization
<|reference_start|>Targeted synthetic data generation for tabular data via hardness characterization: Synthetic data generation has been proven successful in improving model performance and robustness in the context of scarce or low-quality data. Using the data valuation framework to statistically identify beneficial and detrimental observations, we introduce a novel augmentation pipeline that generates only high-value training points based on hardness characterization. We first demonstrate via benchmarks on real data that Shapley-based data valuation methods perform comparably with learning-based methods in hardness characterisation tasks, while offering significant theoretical and computational advantages. Then, we show that synthetic data generators trained on the hardest points outperform non-targeted data augmentation on simulated data and on a large scale credit default prediction task. In particular, our approach improves the quality of out-of-sample predictions and it is computationally more efficient compared to non-targeted methods.<|reference_end|>
@article{ferracci2024targeted,
    title={Targeted synthetic data generation for tabular data via hardness
  characterization},
    author={Tommaso Ferracci, Leonie Tabea Goldmann, Anton Hinel, Francesco Sanna
  Passino},
    journal={arXiv preprint arXiv:2410.00759},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00759},
    primaryClass={cs.LG stat.ML}
}