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We study covariate shift in the context of nonparametric regression.
On estimating regression
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Negative moments of positive random variables
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Improving predictive inference under covariate shift by weighting the log-likelihood function
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A. B. Tsybakov · 2004
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Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
J. Blitzer, M. Dredze, and F. Pereira · 2007
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Direct importance estimation with model selection and its application to covariate shift adaptation
M. Sugiyama, S. Nakajima, H. Kashima, P. V. Buenau, and M. Kawanabe · 2008
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Domain adaptation: Learning bounds and algorithms
Y. Mansour, M. Mohri, and A. Rostamizadeh · 2009
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Multiple source adaptation and the rényi divergence
Y. Mansour, M. Mohri, and A. Rostamizadeh · 2009
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Dataset Shift in Machine Learning
J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence · 2009
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A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010
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Impossibility theorems for domain adaptation
Density ratio estimation in machine learning
M. Sugiyama, T. Suzuki, and T. Kanamori · 2012
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A pac-bayesian approach for domain adaptation with specialization to linear classifiers
P. Germain, A. Habrard, F. Laviolette, and E. Morvant · 2013
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On acoustic emotion recognition: Compensating for covariate shift
A. Hassan, R. Damper, and M. Niranjan · 2013
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Lipschitz density-ratios, structured data, and data-driven tuning
S. Kpotufe · 2017
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Adaptation based on generalized discrepancy
C. Cortes, M. Mohri, and A. M. Medina · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
M. J. Wainwright · 2019
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Application of covariate shift adaptation techniques in brain–computer interfaces
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Adapting visual category models to new domains
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