  Data augmentation is becoming essential for improving regression performance
in critical applications including manufacturing, climate prediction, and
finance. Existing techniques for data augmentation largely focus on
classification tasks and do not readily apply to regression tasks. In
particular, the recent Mixup techniques for classification have succeeded in
improving the model performance, which is reasonable due to the characteristics
of the classification task, but has limitations in regression. We show that
mixing examples that have large data distances using linear interpolations may
have increasingly-negative effects on model performance. Our key idea is thus
to limit the distances between examples that are mixed. We propose RegMix, a
data augmentation framework for regression that learns for each example how
many nearest neighbors it should be mixed with for the best model performance
using a validation set. Our experiments conducted both on synthetic and real
datasets show that RegMix outperforms state-of-the-art data augmentation
baselines applicable to regression.
