REVIEW 9 cited by
Conformal Prediction Under Covariate Shift
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
Signed reviews
read the original 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.
Forward citations
Cited by 9 Pith papers
-
Conformal prediction with localization
Localized conformal prediction reweights training scores by proximity to the test point and adjusts the quantile level to preserve distribution-free finite-sample coverage.
-
Silent coverage failures in rare-event searches and a degeneracy index that predicts them
A Poisson-Fisher degeneracy index (β, γ) predicts which endpoint of a rare-event search interval is vulnerable to model misspecification and whether a goodness-of-fit test can detect the distortion.
-
Locally Optimized Random Forests
A random forest that reweights training data by an estimated likelihood ratio between training and test covariate distributions improves prediction under covariate shift, with modest and uneven real-data gains.
-
With Malice Towards None: Assessing Uncertainty via Equalized Coverage
A conformal prediction wrapper that guarantees each protected group's prediction interval covers the true value at the target rate, using only an exchangeability assumption.
-
SCOPE: Selective Conformal Optimized Pairwise LLM Judging
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.
-
Differentially Private Conformal Prediction via Quantile Binary Search
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.
-
Conformal Prediction for Verifiable Learned Query Optimization
Conformal prediction can turn a learned query optimizer's cost estimates into latency bounds with a user-chosen confidence level, enabling pre-execution verification and a search heuristic that improves planning time.
-
Conformal prediction without knowledge of labeled calibration data
Using predicted labels for conformal calibration gives coverage at least 1-α-β for a model with known error rate β.
-
Conformal Prediction for Privacy-Preserving Machine Learning
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.
Discussion (0). Continue with ORCID to comment.