Title: Robust Covariate Shift Prediction with General Losses and Feature Views,
Abstract: Covariate shift relaxes the widely-employed independent and identically
distributed (IID) assumption by allowing different training and testing input
distributions. Unfortunately, common methods for addressing covariate shift by
trying to remove the bias between training and testing distributions using
importance weighting often provide poor performance guarantees in theory and
unreliable predictions with high variance in practice. Recently developed
methods that construct a predictor that is inherently robust to the
difficulties of learning under covariate shift are restricted to minimizing
logloss and can be too conservative when faced with high-dimensional learning
tasks. We address these limitations in two ways: by robustly minimizing various
loss functions, including non-convex ones, under the testing distribution; and
by separately shaping the influence of covariate shift according to different
feature-based views of the relationship between input variables and example
labels. These generalizations make robust covariate shift prediction applicable
to more task scenarios. We demonstrate the benefits on classification under
covariate shift tasks.