ADA: A Powerful Data Augmentation Technique for Improved Regression Robustness

ADA introduces a clustering-based data augmentation approach that improves regression performance, particularly in low-data scenarios. While improvements are marginal on some datasets, ADA is competitive with or outperforms other methods. The technique is versatile and applicable across regression problems. However, careful consideration is needed to avoid potential pitfalls of overfitting and unrealistic patterns.

Table of Links

Abstract and 1 Introduction

2 Background

2.1 Data Augmentation

2.2 Anchor Regression

3 Anchor Data Augmentation

3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure

3.3 Algorithm

4 Experiments and 4.1 Linear synthetic data

4.2 Housing nonlinear regression

4.3 In-distribution Generalization

4.4 Out-of-distribution Robustness

5 Conclusion, Broader Impact, and References

A Additional information for Anchor Data Augmentation

B Experiments

5 Conclusion

We introduced Anchor Data Augmentation (ADA), an extension of Anchor Regression ...