@article{BickelBruecknerScheffer2009,
  author    = {Bickel, Steffen and Brueckner, Michael and Scheffer, Tobias},
  title     = {Discriminative learning under covariate shift},
  issn      = {1532-4435},
  year      = {2009},
  abstract  = {We address classification problems for which the training instances are governed by an input distribution that is allowed to differ arbitrarily from the test distribution-problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. The problem of learning under covariate shift can be written as an integrated optimization problem. Instantiating the general optimization problem leads to a kernel logistic regression and an exponential model classifier for covariate shift. The optimization problem is convex under certain conditions; our findings also clarify the relationship to the known kernel mean matching procedure. We report on experiments on problems of spam filtering, text classification, and landmine detection.},
  language  = {en}
}