Dimension Reduction for Robust Covariate Shift Correction

@article{Wang2017DimensionRF,
  title={Dimension Reduction for Robust Covariate Shift Correction},
  author={Fulton Wang and Cynthia Rudin},
  journal={CoRR},
  year={2017},
  volume={abs/1711.10938}
}
In the covariate shift learning scenario, the training and test covariate distributions differ, so that a predictor’s average loss over the training and test distributions also differ. In this work, we explore the potential of extreme dimension reduction, i.e. to very low dimensions, in improving the performance of importance weighting methods for handling… CONTINUE READING