Pith. sign in

REVIEW 2 cited by

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1908.08558 v3 pith:WKZ4JWIK submitted 2019-08-22 math.ST stat.MEstat.TH

Conformal prediction with localization

classification math.ST stat.MEstat.TH
keywords conformalpredictioninferencelocalizedsamplelocalizationmethodtest
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We propose a new method called localized conformal prediction, where we can perform conformal inference using only a local region around a new test sample to construct its confidence interval. Localized conformal inference is a natural extension to conformal inference. It generalizes the method of conformal prediction to the case where we can break the data exchangeability, so as to give the test sample a special role. To our knowledge, this is the first work that introduces such a localization to the framework of conformal prediction. We prove that our proposal can also have assumption-free and finite sample coverage guarantees, and we compare the behaviors of localized conformal prediction and conformal prediction in simulations.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

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

  1. Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring

    cs.LG 2026-04 unverdicted novelty 7.0

    A model-agnostic adaptive conformal anomaly detection approach uses weighted quantile bounds learned from past foundation model predictions to deliver interpretable p-value scores with stable calibration under shifts ...

  2. A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

    cs.LG 2021-07 unverdicted novelty 5.0

    Pith review generated a malformed one-line summary.