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Classification with Valid and Adaptive Coverage

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arxiv 2006.02544 v1 pith:XESM5X3R submitted 2020-06-03 stat.ME stat.ML

Yaniv Romano , Matteo Sesia , Emmanuel J. Candès This is my paper · ORCID
classification stat.MEstat.ML
keywords coveragemethodsadaptiveclassificationdatademonstratemarginaladdition
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Conformal inference, cross-validation+, and the jackknife+ are hold-out methods that can be combined with virtually any machine learning algorithm to construct prediction sets with guaranteed marginal coverage. In this paper, we develop specialized versions of these techniques for categorical and unordered response labels that, in addition to providing marginal coverage, are also fully adaptive to complex data distributions, in the sense that they perform favorably in terms of approximate conditional coverage compared to alternative methods. The heart of our contribution is a novel conformity score, which we explicitly demonstrate to be powerful and intuitive for classification problems, but whose underlying principle is potentially far more general. Experiments on synthetic and real data demonstrate the practical value of our theoretical guarantees, as well as the statistical advantages of the proposed methods over the existing alternatives.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 24 citations worldwide. Full citation record

  1. Catching Disguised Transients with ASTRANet: Anomaly-Aware Spectroscopic Classification and Conformal Calibration

    astro-ph.IM 2026-07 conditional novelty 6.0 of 10

    ASTRANet combines a redshift-free spectral classifier, a 16-score anomaly detector, and conformal prediction to identify and calibrate uncertainty for out-of-taxonomy astronomical transients.

  2. Test-time augmentation improves efficiency in conformal prediction

    cs.LG 2025-05 conditional novelty 6.0 of 10

    Applying learned test-time augmentation before conformal scoring reduces prediction set sizes by 10-14% with no loss of nominal coverage.

  3. A Unified Comparative Study with Generalized Conformity Scores for Multi-Output Conformal Regression

    stat.ML 2025-01 accept novelty 6.0 of 10

    New CDF-based and latent-space conformity scores give multi-output conformal predictors asymptotic conditional coverage while retaining finite-sample marginal coverage.

  4. Parametric Scaling Law of Tuning Bias in Conformal Prediction

    cs.LG 2025-02 reject novelty 5.0 of 10

    Using the same data for tuning and calibration in conformal prediction introduces only small coverage bias for simple tuners, and this 'tuning bias' scales up with parameter count and down with calibration set size.

  5. Confidence-Aware Deep Learning for Load Plan Adjustments in the Parcel Service Industry

    cs.LG 2024-11 conditional novelty 5.0 of 10

    A two-stage deep learning framework with conformal prediction predicts inbound parcel load processing buildings and sorts, reporting 99% building and 87% sort accuracy one week ahead, with a 5% overall sort accuracy g...

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