How to Implement ADA for Data Augmentation in Nonlinear Regression Models

Authors:

(1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland ([email protected]);

(2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland ([email protected]);

(3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland ([email protected]).

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

3.3 Algorithm

Finally, in this section, we present the ADA algorithm step by step (Algorithm 1) to generate minibatches of data that can be used to train neural networks (or any other nonlinear regressor) by any stochastic gradient descent method. As discussed previously, we propose to repeat the augmentation with different parameter combinations for each minibatch.

This paper is available on arxiv under CC0 1.0 DEED license.