Domain Adaptation under Target and Conditional Shift

Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang
Proceedings of the 30th International Conference on Machine Learning, PMLR 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 (\it target shift), (2) the marginal P_Y is fixed, while the conditional P_X|Y changes with certain constraints (\it conditional shift), and (3) the marginal P_Y changes, and the conditional P_X|Y changes with constraints (\it 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 \it 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v28-zhang13d, title = {Domain Adaptation under Target and Conditional Shift}, author = {Zhang, Kun and Schölkopf, Bernhard and Muandet, Krikamol and Wang, Zhikun}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {819--827}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {3}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/zhang13d.pdf}, url = {https://proceedings.mlr.press/v28/zhang13d.html}, 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 (\it target shift), (2) the marginal P_Y is fixed, while the conditional P_X|Y changes with certain constraints (\it conditional shift), and (3) the marginal P_Y changes, and the conditional P_X|Y changes with constraints (\it 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 \it 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.} }
Endnote
%0 Conference Paper %T Domain Adaptation under Target and Conditional Shift %A Kun Zhang %A Bernhard Schölkopf %A Krikamol Muandet %A Zhikun Wang %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-zhang13d %I PMLR %P 819--827 %U https://proceedings.mlr.press/v28/zhang13d.html %V 28 %N 3 %X 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 (\it target shift), (2) the marginal P_Y is fixed, while the conditional P_X|Y changes with certain constraints (\it conditional shift), and (3) the marginal P_Y changes, and the conditional P_X|Y changes with constraints (\it 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 \it 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.
RIS
TY - CPAPER TI - Domain Adaptation under Target and Conditional Shift AU - Kun Zhang AU - Bernhard Schölkopf AU - Krikamol Muandet AU - Zhikun Wang BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/05/26 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-zhang13d PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 3 SP - 819 EP - 827 L1 - http://proceedings.mlr.press/v28/zhang13d.pdf UR - https://proceedings.mlr.press/v28/zhang13d.html AB - 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 (\it target shift), (2) the marginal P_Y is fixed, while the conditional P_X|Y changes with certain constraints (\it conditional shift), and (3) the marginal P_Y changes, and the conditional P_X|Y changes with constraints (\it 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 \it 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. ER -
APA
Zhang, K., Schölkopf, B., Muandet, K. & Wang, Z.. (2013). Domain Adaptation under Target and Conditional Shift. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(3):819-827 Available from https://proceedings.mlr.press/v28/zhang13d.html.

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