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- W4288373770 abstract "We extend conformal prediction methodology beyond the case of exchangeable data. In particular, we show that a weighted version of conformal prediction can be used to compute distribution-free prediction intervals for problems in which the test and training covariate distributions differ, but the likelihood ratio between these two distributions is known---or, in practice, can be estimated accurately with access to a large set of unlabeled data (test covariate points). Our weighted extension of conformal prediction also applies more generally, to settings in which the data satisfies a certain weighted notion of exchangeability. We discuss other potential applications of our new conformal methodology, including latent variable and missing data problems." @default.
- W4288373770 created "2022-07-29" @default.
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- W4288373770 date "2019-04-11" @default.
- W4288373770 modified "2023-10-16" @default.
- W4288373770 title "Conformal Prediction Under Covariate Shift" @default.
- W4288373770 doi "https://doi.org/10.48550/arxiv.1904.06019" @default.
- W4288373770 hasPublicationYear "2019" @default.
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