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Conformal Prediction Under Covariate Shift

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arxiv 1904.06019 v3 pith:CS6F23RD submitted 2019-04-12 stat.ME

Ryan J. Tibshirani , Rina Foygel Barber , Emmanuel J. Candes , Aaditya Ramdas This is my paper · ORCID
classification stat.ME
keywords conformalpredictiondatacovariateweighteddistributionsmethodologyproblems
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SCOPE: Selective Conformal Optimized Pairwise LLM Judging

    cs.CL 2026-02 conditional novelty 5.0 of 10

    A conformal calibration method (SCOPE) plus a bidirectional entropy score (BPE) lets LLM pairwise judges abstain selectively while keeping accepted-set error below a user-specified bound.

  2. Differentially Private Conformal Prediction via Quantile Binary Search

    stat.ME 2025-07 conditional novelty 5.0 of 10

    P-COQS builds differentially private conformal prediction sets by replacing the calibration quantile with a binary-search privatized quantile, yielding approximate coverage with a computable error bound.

  3. Conformal prediction without knowledge of labeled calibration data

    stat.ME 2025-09 conditional novelty 4.0 of 10

    Using predicted labels for conformal calibration gives coverage at least 1-α-β for a model with known error rate β.

  4. Conformal Prediction for Privacy-Preserving Machine Learning

    cs.LG 2025-07 conditional novelty 4.0 of 10

    Conformal prediction provides valid coverage on AES-encrypted MNIST when a single fixed key is used, but the e-value method gives large sets and the p-value method is more compact.

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