Data augmentation is a cornerstone of the machine learning pipeline, yet its
theoretical underpinnings remain unclear. Is it merely a way to artificially
augment the data set size? Or is it about encouraging the model to satisfy
certain invariance? In this work we consider another angle, and we study the
effect of data augmentation on the dynamic of the learning process. We find
that data augmentation can alter the relative importance of various features,
effectively making certain informative but hard to learn features more likely
to be captured in the learning process. Importantly, we show that this effect
is more pronounced for non-linear models, such as neural networks. Our main
contribution is a detailed analysis of data augmentation on the learning
dynamic for a two layer convolutional neural network in the recently proposed
multi-view data model by Allen-Zhu and Li [2020]. We complement this analysis
with further experimental evidence that data augmentation can be viewed as
feature manipulation.

本文研究数据增强对学习过程动态的影响，发现数据增强可以改变各种特征的相对重要性，特别是对于神经网络等非线性模型更为明显，可以被看作是特征操作。

数据增强作为特征操作

Data Augmentation as Feature Manipulation

We consider an online regression setting in which individuals adapt to the
regression model: arriving individuals are aware of the current model, and
invest strategically in modifying their own features so as to improve the
predicted score that the current model assigns to them. Such feature
manipulation has been observed in various scenarios -- from credit assessment
to school admissions -- posing a challenge for the learner. Surprisingly, we
find that such strategic manipulations may in fact help the learner recover the
meaningful variables -- that is, the features that, when changed, affect the
true label (as opposed to non-meaningful features that have no effect). We show
that even simple behavior on the learner's part allows her to simultaneously i)
accurately recover the meaningful features, and ii) incentivize agents to
invest in these meaningful features, providing incentives for improvement.

研究在在线回归模型中，个体会通过改变自己的特征来改进模型所预测的得分，并探讨在这一过程中，如何精准地找出对结果有意义的特征，并通过激励机制鼓励学习模型学习这些特征。