Dataset Shift in Machine Learning
Dataset Shift in Machine Learning
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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.) 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 book 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.
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Front Matter
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I Introduction to Dataset Shift
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II Theoretical Views on Dataset and Covariate Shift
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III Algorithms for Covariate Shift
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6
Geometry of Covariate Shift with Applications to Active Learning
Kanamori Takafumi andShimodaira Hidetoshi
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7
A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift
Sugiyama Masashi and others
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8
Covariate Shift by Kernel Mean Matching
Arthur Gretton and others
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9
Discriminative Learning under Covariate Shift with a Single Optimization Problem
Bickel Amir and others
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10
An Adversarial View of Covariate Shift and a Minimax Approach
Globerson Amir and others
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6
Geometry of Covariate Shift with Applications to Active Learning
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IV Discussion
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End Matter
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