Anchor Data Augmentation
This is an incremental improvement for data augmentation in regression tasks, potentially benefiting machine learning practitioners working with robust predictions.
The paper tackles data augmentation for nonlinear over-parametrized regression by proposing Anchor Data Augmentation (ADA), which extends Anchor regression to create more training examples, resulting in competitive performance with state-of-the-art C-Mixup solutions.
We propose a novel algorithm for data augmentation in nonlinear over-parametrized regression. Our data augmentation algorithm borrows from the literature on causality and extends the recently proposed Anchor regression (AR) method for data augmentation, which is in contrast to the current state-of-the-art domain-agnostic solutions that rely on the Mixup literature. Our Anchor Data Augmentation (ADA) uses several replicas of the modified samples in AR to provide more training examples, leading to more robust regression predictions. We apply ADA to linear and nonlinear regression problems using neural networks. ADA is competitive with state-of-the-art C-Mixup solutions.