Back to feed
arXiv cs.LG
arXiv cs.LG
6/30/2026
Counterfactual Residual Data Augmentation for Regression

Counterfactual Residual Data Augmentation for Regression

Short summary

Novel CRDA technique improves tabular regression by generating synthetic training data through residual invariance modeling. Achieves 22.9% MSE reduction for MLPs, 6.4% for XGBoost. Model-agnostic approach designed for limited, noisy datasets.

  • •CRDA generates synthetic data by modeling residual invariance under feature perturbations
  • •22.9% MSE reduction for MLP, 6.4% for XGBoost vs. baselines
  • •Model-agnostic technique applicable to any regressor

Generated with AI, which can make mistakes.

#research-breakthrough#ai-tools
Read full article at arXiv cs.LG

Is this a good recommendation for you?

Comments

Failed to load comments. Please try again.

Explore more

Mind the Residual Gap: Probabilistic Downscaling under Real-World Bias
arXiv cs.LG
WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning
arXiv cs.LG
Designing synthetic datasets for the real world: Mechanism design and reasoning from first principles
Google Research
Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting
arXiv cs.LG