Skip to yearly menu bar Skip to main content


Oral

A new similarity measure for covariate shift with applications to nonparametric regression

Reese Pathak · Cong Ma · Martin Wainwright

Hall F

Abstract:

We study covariate shift in the context of nonparametric regression. We introduce a new measure of distribution mismatch between the source and target distributions using the integrated ratio of probabilities of balls at a given radius. We use the scaling of this measure with respect to the radius to characterize the minimax rate of estimation over a family of Hölder continuous functions under covariate shift. In comparison to the recently proposed notion of transfer exponent, this measure leads to a sharper rate of convergence and is more fine-grained. We accompany our theory with concrete instances of covariate shift that illustrate this sharp difference.

Chat is not available.