Official Python implementation of "Information Geometrically Generalized Covariate Shift Adaptation" by Masanari Kimura and Hideitsu Hino
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, which is one of the most important research topics in machine learning. We show that the well-known family of methods for covariate shift adaptation can be unified in the framework of information geometry. Furthermore, we show that parameter search for geometrically generalized methods of covariate shift adaptation can be achieved efficiently. Numerical experiments show that our generalization can always achieve better performance than the existing methods it encompasses.