Paper Citation Record · LEDGER

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels

As of 11 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2501.12749.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.12749 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:59:29.066195Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T21:32:12.522751Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-08T21:32:12.619024Z

Reference resolution

26 of 26 outbound references displayed

verified exact0
verified fuzzy21
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 89136b64-e923-4ec2-a0fa-9c498bcfbde0 · outbound

This paper cites Angelopoulos, Stephen Bates, Jitendra Malik, and Michael I Jordan.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Angelopoulos, Stephen Bates, Jitendra Malik, and Michael I Jordan

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.457816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation de128f55-f13c-4734-936c-fdd6a6c1eb16 · outbound

This paper cites Conformal prediction: A gentle introduction.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Conformal prediction: A gentle introduction

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.442452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 70f91e75-49d7-400b-bdcb-e6a780ca195b · outbound

This paper cites Split conformal prediction under data contamination.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Split conformal prediction under data contamination

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.425772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d774e2d4-150a-41b3-b372-527db19d3745 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Imagenet: A large-scale hierarchical image database

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.411407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2bb640e4-5d0f-4a4e-b8d6-63181e7c862d · outbound

This paper cites Label Noise Robustness of Conformal Prediction.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Label Noise Robustness of Conformal Prediction

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T16:59:28.971035Z

Source-reported events for the cited work

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Observation 95a3b941-21e5-4ad1-8249-e3cb094d14c6 · outbound

This paper cites Deep learning with label differential privacy.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Deep learning with label differential privacy

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.397642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c701dbe3-9512-47d9-bf36-7d0b6ba44b7f · outbound

This paper cites On calibration of modern neural networks.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels On calibration of modern neural networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.382879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f4abb2bd-182e-4cd5-ab15-f3bd1290c70f · outbound

This paper cites Deep residual learning for image recognition.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Deep residual learning for image recognition

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.367589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 92737277-88e2-4b10-9fe7-f2dfe62db333 · outbound

This paper cites Learning multiple layers of features from tiny images.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Learning multiple layers of features from tiny images

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T16:59:28.992771Z

Source-reported events for the cited work

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Observation f5b8838b-70ad-4b9d-8824-fb7fa6c28eda · outbound

This paper cites Provably end-to-end label-noise learning without anchor points.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Provably end-to-end label-noise learning without anchor points

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.343257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ea455038-4c4f-43ad-aec1-37903eadd518 · outbound

This paper cites A holistic view of label noise transition matrix in deep learning and beyond.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels A holistic view of label noise transition matrix in deep learning and beyond

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.328331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cb43bdaa-ad88-449d-9428-42823b6f0ecf · outbound

This paper cites Improving trustworthiness of AI disease severity rating in medical imaging with ordinal conformal prediction sets.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Improving trustworthiness of AI disease severity rating in medical imaging with ordinal conformal prediction sets

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.312735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8155984d-0d26-47dd-8896-0e998c9acef8 · outbound

This paper cites Fair conformal predictors for applications in medical imaging.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Fair conformal predictors for applications in medical imaging

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.297235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c308cf50-24f6-4340-b8a8-c5ef86b403cd · outbound

This paper cites The tight constant in the D voretzky- K iefer- W olfowitz inequality.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels The tight constant in the D voretzky- K iefer- W olfowitz inequality

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.281945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d0604798-d1ee-4a3a-8608-da90d16d8c2e · outbound

This paper cites Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.266821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 973043c4-8ccb-4e7a-8655-b30ed633a588 · outbound

This paper cites A conformal prediction score that is robust to label noise.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels A conformal prediction score that is robust to label noise

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.250817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f6512c6c-9a1c-435f-9ddb-bc4b8deac64d · outbound

This paper cites Confidence calibration of a medical imaging classification system that is robust to label noise.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Confidence calibration of a medical imaging classification system that is robust to label noise

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.236473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dc6894d7-1201-417d-816e-fc16703eb9cd · outbound

This paper cites Privacy-preserving conformal prediction under local differential privacy.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Privacy-preserving conformal prediction under local differential privacy

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.223108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8d46fc8e-db65-4f09-9862-ac479bd6623e · outbound

This paper cites Classification with valid and adaptive coverage.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Classification with valid and adaptive coverage

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.209451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c99277f3-6857-4ba2-a5ba-c92d51c2435c · outbound

This paper cites Adaptive conformal classification with noisy labels.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Adaptive conformal classification with noisy labels

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.194609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a301b952-ffa6-4c1d-a274-a8516645f02c · outbound

This paper cites Learning from noisy labels with deep neural networks: A survey.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Learning from noisy labels with deep neural networks: A survey

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.178826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f4075945-23ce-4145-9086-5cb332d0d5e6 · outbound

This paper cites Algorithmic learning in a random world, volume 29.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Algorithmic learning in a random world, volume 29

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T16:59:29.047676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a5ee840-9f86-41e1-a51c-9cb25ba4ee59 · outbound

This paper cites Robust medical image classification from noisy labeled data with global and local representation guided co-training.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Robust medical image classification from noisy labeled data with global and local representation guided co-training

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.153705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 24123c8c-833a-4f0d-a2a7-a9d0d5ace082 · outbound

This paper cites Learning noise transition matrix from only noisy labels via total variation regularization.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels Learning noise transition matrix from only noisy labels via total variation regularization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:59:29.138156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 760f728c-9633-4d8e-b14b-32691d187785 · outbound

This paper cites , " * write output.state after.block = add.period write.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels , " * write output.state after.block = add.period write

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T16:59:29.061351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2070cdd-199c-47e8-b939-ae9740a14ef9 · outbound

This paper cites write newline.

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels write newline

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T16:59:29.066195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation cc00fe33-f7b4-49d8-a518-e8dde5576091 · inbound

Robust Conformal Outlier Detection under Contaminated Reference Data cites this paper.

Robust Conformal Outlier Detection under Contaminated Reference Data Conformal Prediction of Classifiers with Many Classes based on Noisy Labels

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-08T21:32:12.626195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.