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Dataset Shift in Machine Learning
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Dataset Shift in Machine Learning

Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence

Abstract

Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on ... More

Keywords: dataset shift, predictive modeling, covariate shift, input distribution, practical applications, bias, experimental design, irreproducibility, testing conditions, email spam filtering

Bibliographic Information

Print publication date: 2008 Print ISBN-13: 9780262170055
Published to MIT Press Scholarship Online: August 2013 DOI:10.7551/mitpress/9780262170055.001.0001

Authors

Affiliations are at time of print publication.

Joaquin Quiñonero-Candela, editor

Masashi Sugiyama, editor

Anton Schwaighofer, editor

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Contents

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I Introduction to Dataset Shift

II Theoretical Views on Dataset and Covariate Shift

III Algorithms for Covariate Shift

6 Geometry of Covariate Shift with Applications to Active Learning

Kanamori Takafumi, and Shimodaira Hidetoshi

8 Covariate Shift by Kernel Mean Matching

Arthur Gretton, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt, and Bernhard Schölkopf

10 An Adversarial View of Covariate Shift and a Minimax Approach

Globerson Amir, Hui Teo Choon, Smola Alex, and Roweis Sam

IV Discussion

11 Author Comments

Shimodaira Hidetoshi, Sugiyama Masashi, Storkey Amos, Gretton Arthur, and David Shai-Ben