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Inductive Confidence Machines for Regression
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In the citing paper
“SCP offers distribution-free, model-agnostic guarantees on groundtruth coverage, assuming data exchangeability.”
From Conformal Lesion Segmentation for 3D Medical Images (2025) · cites this paper as Papadopoulos et al. 2002
In the citing paper
“Our procedure, based on inductive (or 'split') Conformal Prediction (CP) [21, 15], guarantees that at a user-chosen confidence level 1 -α , the inner mask contains at most a userspecified fraction τ of false-positive pixels.”
From Controlling False Positives in Image Segmentation via Conformal Prediction (2025) · cites this paper as [15]
In the citing paper
“Split CP [15] constructs distribution-free prediction sets with finite-sample guarantees of containing the true target at a user-specified confidence level 1 -α .”
From Controlling False Positives in Image Segmentation via Conformal Prediction (2025) · cites this paper as [15]
In the citing paper
“Importantly, CP provides marginal frequentist guarantees on the mask-level procedure , rather than on individual pixels”
From Controlling False Positives in Image Segmentation via Conformal Prediction (2025) · cites this paper as [15]
In the citing paper
“Our method builds on the standard inductive CP framework [15].”
From Controlling False Positives in Image Segmentation via Conformal Prediction (2025) · cites this paper as [15]
Citations
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Metadata
- Authors
- Papadopoulos, H., Proedrou, K., Vovk, V., & Gammerman, A.
- Affiliations
- Royal Holloway University of London (GB)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2002
- Pages
- 345-356
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W1499689786
- Cited by
- 346
- References
- 19
- Citations by year
- 2026: 73 · 2025: 61 · 2024: 50 · 2023: 38 · 2022: 17 · 2021: 17 · 2020: 18 · 2019: 5 · 2018: 4 · 2017: 7 · 2016: 3 · 2015: 3 · 2014: 15 · 2013: 4 · 2012: 6
- FWCI
- 0.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- confidence estimation, regression analysis, inductive reasoning, transductive inference, ridge regression, computational efficiency, large data sets
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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