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了因果推断领域的文献，并扩展了近期提出的用于数据增强的锚点回归（Anchor regression, AR）方法，这与当前依赖于Mixup文献的、最先进的与领域无关的解决方案形成对比。我们的锚点数据增强（Anchor Data Augmentation, ADA）使用AR中修改后样本的多个副本提供更多训练实例，从而得到更稳健的回归预测。我们使用神经网络将ADA应用于线性和非线性回归问题。ADA与最先进的C-Mixup解决方案具有竞争力。