arXiv:2606.28460v1 Announce Type: new Abstract: Data-driven modeling in real-world regression tasks often suffers from limited training samples, high collection costs, and noisy observations. Inspired by the impact of data augmentation in vision and language, we propose a novel Counterfactual Residual Data Augmentation (CRDA) technique for tabular regression. Our key insight is that once a regressor has modeled the systematic component of the data, the remaining noise can be viewed as an invariant residual that remains stable under small perturbations of carefully selected features. We exploit
The continuous drive to improve data-driven models, especially in scenarios with limited or noisy data, pushes for novel augmentation techniques like CRDA, building on existing successes in other AI domains.
This research addresses a fundamental challenge in data-driven modeling: the scarcity and quality of training data, offering a pathway to more robust and generalized regression models across various applications.
The explicit treatment of noise as an invariant residual for counterfactual data augmentation could significantly improve model performance and reliability in data-scarce or noisy real-world regression tasks.
- · AI/ML researchers and developers
- · Industries with high data collection costs (e.g., healthcare, finance)
- · Small data analytics platforms
- · Regression-based predictive modeling tools
- · Traditional data augmentation methods limited to noise addition
- · Systems highly reliant on large, perfectly clean datasets
- · Competitors without advanced data generation techniques
Improved accuracy and robustness of regression models, especially with limited data.
Reduced dependence on massive, expensive datasets, democratizing advanced AI applications for more sectors.
Acceleration of AI adoption in domains previously constrained by data availability and quality, leading to new predictive insights and automated decision-making.
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Read at arXiv cs.LG