A data set sampled from a certain population is biased if the subgroups of
the population are sampled at proportions that are significantly different from
their underlying proportions. Training machine learning models on biased data
sets requires correction techniques to compensate for the bias. We consider two
commonly-used techniques, resampling and reweighting, that rebalance the
proportions of the subgroups to maintain the desired objective function. Though
statistically equivalent, it has been observed that resampling outperforms
reweighting when combined with stochastic gradient algorithms. By analyzing
illustrative examples, we explain the reason behind this phenomenon using tools
from dynamical stability and stochastic asymptotics. We also present
experiments from regression, classification, and off-policy prediction to
demonstrate that this is a general phenomenon. We argue that it is imperative
to consider the objective function design and the optimization algorithm
together while addressing the sampling bias.

考虑到数据集采样偏差影响机器学习模型的效果，本研究分析重新采样和重新加权等多种技术，并通过实验和理论工具从动态稳定性和随机渐近性方面阐述重新采样的优越性。我们强调在处理采样偏差时应该同时考虑目标函数设计和优化算法。