Robust Covariate Shift Regression

Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart

Abstract: In many learning settings, the source data available to train a regression model differs from the target data it encounters when making predictions due to input distribution shift. Appropriately dealing with this situation remains an important challenge. Existing methods attempt to “reweight” the source data samples to better represent the target domain, but this introduces strong inductive biases that are highly extrapolative and can often err greatly in practice. We propose a robust approach for regression under covariate shift that embraces the uncertainty resulting from sample selection bias by producing regression models that are explicitly robust to it. We demonstrate the benefits of our approach on a number of regression tasks.