Importance Weighting Correction of Regularized Least-Squares for Covariate and Target Shifts. (arXiv:2210.09709v2 [stat.ML] UPDATED)

In many real world problems, the training data and test data have different
distributions. This situation is commonly referred as a dataset shift. The most
common settings for dataset shift often considered in the literature are {\em
covariate shift } and {\em target shift}. Importance weighting (IW) correction
is a universal method for correcting the bias present in learning scenarios
under dataset shift. The question one may ask is: does IW correction work
equally well for different dataset shift scenarios? …

arxiv importance least squares