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.

翻译：我们提出了一种用于非线性过参数化回归中数据增强的新算法。该数据增强算法借鉴因果推断领域的研究，扩展了近期提出的锚点回归（Anchor regression, AR）方法——这与当前依赖Mixup文献的最新领域无关解决方案形成对比。我们的锚点数据增强（Anchor Data Augmentation, ADA）利用AR中修正样本的多重复制来提供更多训练样本，从而获得更稳健的回归预测。我们将ADA应用于基于神经网络的线性和非线性回归问题。实验表明，ADA与当前最先进的C-Mixup解决方案具有同等竞争力。