Testing ADA on Synthetic and Real-World Data

This section reports comprehensive experimental results on ADA, including performance with synthetic linear data and various real-world regression datasets. ADA outperforms other augmentation methods like C-Mixup, especially when data is scarce. Hyperparameters such as cluster number and γ range are explored, with results showing ADA’s consistency and robustness across different scenarios.

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

B.1 Linear synthetic data

In this section, we present more detailed results of the experiments on ...