nocotan / igiwerm

Information Geometrically Generalized Covariate Shift Adaptation

Information Geometrically Generalized Covariate Shift Adaptation

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.

Citation

igiwerm's Issues

add makefile

add poetry

train_test_split

dataset scripts

add model selection

geometric_functions

directory structure under giwerm

add visualizer

update README: github actions badge

determine the directory structure

add CI

integrate sklearn