Many machine learning methods assume that the training and test data follow the same distribution. However, in the real world, this assumption is very often violated. In particular, the phenomenon that the marginal distribution of the data changes is called covariate shift, one of the most important research topics in machine learning. We show that the well-known family of covariate shift adaptation methods is unified in the framework of information geometry. Furthermore, we show that parameter search for geometrically generalized covariate shift adaptation method can be achieved efficiently. Numerical experiments show that our generalization can achieve better performance than the existing methods it encompasses.

翻译：许多机器学习方法假设训练数据和测试数据服从相同的分布。然而，在现实世界中，这一假设常常被违背。特别是，数据边际分布发生变化的现象被称为协变量偏移，这是机器学习中最重要的研究课题之一。我们表明，著名的协变量偏移适应方法族可以在信息几何框架下得到统一。此外，我们还证明了几何广义协变量偏移适应方法的参数搜索可以高效实现。数值实验表明，我们的广义方法能够比其所涵盖的现有方法取得更优的性能。