Papers › Double-Weighting for Covariate Shift Adaptation
Double-Weighting for Covariate Shift Adaptation
José I. Segovia-Martín, Santiago Mazuelas, Anqi Liu
Supervised learning is often affected by a covariate shift in which the marginal distributions of instances (covariates x) of training and testing samples pₜᵣ(x) and pₜₑ(x) are different but the label conditionals coincide. Existing approaches address such covariate shift by either using the ratio pₜₑ(x)/pₜᵣ(x) to weight training samples (reweighted methods) or using the ratio pₜᵣ(x)/pₜₑ(x) to weight testing samples (robust methods). However, the performance of such approaches can be poor under support mismatch or when the above ratios take large values. We propose a minimax risk classification (MRC) approach for covariate shift adaptation that avoids such limitations by weighting both training and testing samples. In addition, we develop effective techniques that obtain both sets of weights and generalize the conventional kernel mean matching method. We provide novel generalization bounds for our method that show a significant increase in the effective sample size compared with reweighted methods. The proposed method also achieves enhanced classification performance in both synthetic and empirical experiments.
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