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Covariate shift relaxes the widely-employed independent and identically distributed (IID) assumption by allowing different training and testing input distributions.
Sample selection bias as a specification error (with an application to the estimation of labor supply functions), 1977
James J Heckman · 1977
Earlier work this paper cites.
Information theoretical optimization techniques
Flemming Topsøe · 1979
Earlier work this paper cites.
Vehicle recognition using rule based methods
J P Siebert · 1987
Earlier work this paper cites.
UCI repository of machine learning databases, 1998
D. J. Newman, S. Hettich, C. L. Blake, and C. J. Merz · 1998
Earlier work this paper cites.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
Earlier work this paper cites.
On the algorithmic implementation of multiclass kernel-based vector machines
Koby Crammer and Yoram Singer · 2001
Earlier work this paper cites.
Game theory, maximum entropy, minimum discrepancy, and robust Bayesian decision theory
Peter D. Grünwald and A. Phillip Dawid · 2004
Earlier work this paper cites.
Learning and evaluating classifiers under sample selection bias
Bianca Zadrozny · 2004
Earlier work this paper cites.
An improved categorization of classifier’s sensitivity on sample selection bias
Wei Fan, Ian Davidson, Bianca Zadrozny, and Philip S. Yu · 2005
Earlier work this paper cites.
Maximum entropy distribution estimation with generalized regularization
Miroslav Dudík and Robert E. Schapire · 2006
Earlier work this paper cites.
Correcting sample selection bias by unlabeled data
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Covariate shift adaptation by importance weighted cross validation
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Sample selection bias correction theory
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A literature survey on domain adaptation of statistical classifiers
Jing Jiang · 2008
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Direct importance estimation with model selection and its application to covariate shift adaptation
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A survey on transfer learning
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2012
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Domain adaptation and sample bias correction theory and algorithm for regression
Corinna Cortes and Mehryar Mohri · 2014
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Robust classification under sample selection bias
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Doubly robust covariate shift correction
Sashank J Reddi, Barnabás Póczos, and Alex Smola · 2014
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Shift-pessimistic active learning using robust bias-aware prediction
Anqi Liu, Lev Reyzin, and Brian D Ziebart · 2015
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Covariate shift by kernel mean matching
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A minimax approach to supervised learning
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