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Poster
Conformal Prediction Under Covariate Shift
Ryan Tibshirani · Rina Barber · Emmanuel Candes · Aaditya Ramdas

Tue Dec 10 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #213

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 the two distributions is known---or, in practice, can be estimated accurately from a set of unlabeled data (test covariate points). Our weighted extension of conformal prediction also applies more broadly, 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.