Home Page

Papers

Submissions

News

Scope

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Login



RSS Feed

Domain Adaptation under Target and Conditional Shift

Kun Zhang, Bernhard Schlkopf, Krikamol Muandet, Zhikun Wang
;
JMLR W&CP 28 (3) : 819–827, 2013

Abstract

Let \(X\) denote the feature and \(Y\) the target. We consider domain adaptation under three possible scenarios: (1) the marginal \(P_Y\) changes, while the conditional \(P_{X|Y}\) stays the same (target shift), (2) the marginal \(P_Y\) is fixed, while the conditional \(P_{X|Y}\) changes with certain constraints (conditional shift), and (3) the marginal \(P_{Y}\) changes, and the conditional \(P_{X|Y}\) changes with constraints (generalized target shift). Using background knowledge, causal interpretations allow us to determine the correct situation for a problem at hand. We exploit importance reweighting or sample transformation to find the learning machine that works well on test data, and propose to estimate the weights or transformations by reweighting or transforming training data to reproduce the covariate distribution on the test domain. Thanks to kernel embedding of conditional as well as marginal distributions, the proposed approaches avoid distribution estimation, and are applicable for high-dimensional problems. Numerical evaluations on synthetic and real-world datasets demonstrate the effectiveness of the proposed framework.

Related Material