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Dataset Shift in Machine Learning
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Joaquin Quinonero-Candela Masashi Sugiyama

Dataset Shift in Machine Learning

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

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English
MIT Press
07 June 2022
Machine learning
Series: Neural Information Processing series
An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions.

An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions.

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.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift.

Contributors- Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Br ckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert M ller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Sch lkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama
Edited by:   , , ,
Imprint:   MIT Press
Country of Publication:   United States
Dimensions:   Height: 254mm,  Width: 203mm, 
Weight:   369g
ISBN:   9780262545877
ISBN 10:   026254587X
Series:   Neural Information Processing series
Pages:   248
Publication Date:  
Audience:   College/higher education ,  Professional and scholarly ,  Primary ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active

Joaquin Quinonero-Candela Joaquin Quinonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K. Masashi Sugiyama Masashi Sugiyama is Director of the RIKEN Center for Advanced Intelligence Project and Professor of Computer Science at the University of Tokyo. Anton Schwaighofer Anton Schwaighofer is an Applied Researcher in the Online Services and Advertising Group at Microsoft Research, Cambridge, U.K. Neil D. Lawrence Neil D. Lawrence is Senior Lecturer and Member of the Machine Learning and Optimisation Research Group in the School of Computer Science at the University of Manchester.

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