论文标题
使用累积分布函数对采样偏差的强大校正
Robust Correction of Sampling Bias Using Cumulative Distribution Functions
论文作者
论文摘要
不同的域和偏见的数据集可能会导致训练和目标分布之间的差异（称为协变量移动）。当前减轻此方法的方法通常依赖于估计训练和目标概率密度功能的比率。这些技术需要参数调整，并且在不同的数据集中可能不稳定。我们提出了一种使用经验累积分布函数估计目标分布的新方法来处理协变量偏移，该方法通过对Vapnik和Izmailov提出的最新想法的严格概括来估计目标分布。此外，我们从实验上表明，我们的方法在预测中更强大，不依赖参数调整，并且与当前的合成和真实数据集的最新技术相比，分类性能相似。
Varying domains and biased datasets can lead to differences between the training and the target distributions, known as covariate shift. Current approaches for alleviating this often rely on estimating the ratio of training and target probability density functions. These techniques require parameter tuning and can be unstable across different datasets. We present a new method for handling covariate shift using the empirical cumulative distribution function estimates of the target distribution by a rigorous generalization of a recent idea proposed by Vapnik and Izmailov. Further, we show experimentally that our method is more robust in its predictions, is not reliant on parameter tuning and shows similar classification performance compared to the current state-of-the-art techniques on synthetic and real datasets.
