ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2023
Prediction sets adaptive to unknown covariate shift.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Training-set conditionally valid prediction sets with right-censored data.Lifetime data analysis · 2026Article
- Doubly robust calibration of prediction sets under covariate shift.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2024Article
- EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.Annals of statistics · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Predicting sets of outcomes-instead of unique outcomes-is a promising solution to uncertainty quantification in statistical learning. Despite a rich literature on constructing prediction sets with statistical guarantees, adapting to unknown covariate shift-a prevalent issue in practice-poses a serious unsolved challenge. In this article, we show that prediction sets with finite-sample coverage guarantee are uninformative and propose a novel flexible distribution-free method, PredSet-1Step, to efficiently construct prediction sets with an asymptotic coverage guarantee under unknown covariate shift. We formally show that our method is
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.