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Covariate Shift Adaptation: Supervised Learning When Training and Test Inputs Have Different Distributions

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A common assumption in supervised learning is that the input points in the training set follow the same probability distribution as the input points in the test phase. However, this assumption is not satisfied, for example, when the outside of the training region is extrapolated. The situation where the training input points and test input points follow different distributions while the conditional distribution of output values given input points is unchanged is called the covariate shift. Under the covariate shift, standard techniques such as maximum likelihood estimation or cross validation do not work as desired. In this talk, I will introduce covariate shift adaptation techniques which we developed recently.

References:

Sugiyama, M., Krauledat, M., & Mueller, K.-R. Covariate shift adaptation by importance weighted cross validation. Journal of Machine Learning Research, vol.8 (May), pp.985-1005, 2007. “http://sugiyama-www.cs.titech.ac.jp/~sugi/2007/IWCV.pdf

Sugiyama, M., Nakajima, S., Kashima, H., von Buenau, P. & Kawanabe, M., Direct importance estimation with model selection and its application to covariate shift adaptation. Technical Report TR07 -0003, Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan, 2007. pdf file url

This talk is part of the Machine Learning @ CUED series.

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