Paper Citation Record · LEDGER

Preserving AUC Fairness in Learning with Noisy Protected Groups

As of 8 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2505.18532.

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

pith.paper-citation-record.v1
2505.18532 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:37:49.241296Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

59 of 59 outbound references displayed

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External citation measurements

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Outbound references

Observation 3147c283-d2cf-42c7-8983-fdb8226f7fe5 · outbound

This paper cites write newline.

Preserving AUC Fairness in Learning with Noisy Protected Groups write newline

Reference 1

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This paper cites https://www.kaggle.com/c/deepfake-detection-challenge.

Preserving AUC Fairness in Learning with Noisy Protected Groups https://www.kaggle.com/c/deepfake-detection-challenge

Reference 2

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Observation 07819633-572f-448a-b82e-91702393d509 · outbound

This paper cites Uci machine learning repository, 2007.

Preserving AUC Fairness in Learning with Noisy Protected Groups Uci machine learning repository, 2007

Reference 3

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This paper cites H., et al.

Preserving AUC Fairness in Learning with Noisy Protected Groups H., et al

Reference 4

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This paper cites and Haas, C.

Preserving AUC Fairness in Learning with Noisy Protected Groups and Haas, C

Reference 5

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This paper cites E., Huang, L., Keswani, V., and Vishnoi, N.

Preserving AUC Fairness in Learning with Noisy Protected Groups E., Huang, L., Keswani, V., and Vishnoi, N

Reference 6

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Observation bb6c3395-2795-4e3d-b19c-e916be0016af · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

Preserving AUC Fairness in Learning with Noisy Protected Groups Xception: Deep learning with depthwise separable convolutions

Reference 7

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Preserving AUC Fairness in Learning with Noisy Protected Groups and Mohri, M

Reference 8

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Observation fa38e62f-5e72-4e7b-a849-34626fc2a5b6 · outbound

This paper cites Measuring and mitigating unintended bias in text classification.

Preserving AUC Fairness in Learning with Noisy Protected Groups Measuring and mitigating unintended bias in text classification

Reference 9

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This paper cites S., and Pontil, M.

Preserving AUC Fairness in Learning with Noisy Protected Groups S., and Pontil, M

Reference 10

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Observation 9521cfd3-dd03-4ce2-b50e-539153bfc24d · outbound

This paper cites Efficient projections onto the l 1-ball for learning in high dimensions.

Preserving AUC Fairness in Learning with Noisy Protected Groups Efficient projections onto the l 1-ball for learning in high dimensions

Reference 11

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Preserving AUC Fairness in Learning with Noisy Protected Groups Unresolved cited work

Reference 12

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Preserving AUC Fairness in Learning with Noisy Protected Groups Unresolved cited work

Reference 13

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This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Preserving AUC Fairness in Learning with Noisy Protected Groups Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 14

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This paper cites Large Scale Transfer Learning for Tabular Data via Language Modeling.

Preserving AUC Fairness in Learning with Noisy Protected Groups Large Scale Transfer Learning for Tabular Data via Language Modeling

Reference 15

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This paper cites Measuring fairness of rankings under noisy sensitive information.

Preserving AUC Fairness in Learning with Noisy Protected Groups Measuring fairness of rankings under noisy sensitive information

Reference 16

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This paper cites When fair classification meets noisy protected attributes.

Preserving AUC Fairness in Learning with Noisy Protected Groups When fair classification meets noisy protected attributes

Reference 17

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This paper cites Deepfakes dataset by google & jigsaw.

Preserving AUC Fairness in Learning with Noisy Protected Groups Deepfakes dataset by google & jigsaw

Reference 18

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This paper cites Robust attentive deep neural network for detecting gan-generated faces.

Preserving AUC Fairness in Learning with Noisy Protected Groups Robust attentive deep neural network for detecting gan-generated faces

Reference 19

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This paper cites Proxy Fairness.

Preserving AUC Fairness in Learning with Noisy Protected Groups Proxy Fairness

Reference 20

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Preserving AUC Fairness in Learning with Noisy Protected Groups Unresolved cited work

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Preserving AUC Fairness in Learning with Noisy Protected Groups Fairness without demographics in repeated loss minimization

Reference 22

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This paper cites Dualcoop++: Fast and effective adaptation to multi-label recognition with limited annotations.

Preserving AUC Fairness in Learning with Noisy Protected Groups Dualcoop++: Fast and effective adaptation to multi-label recognition with limited annotations

Reference 23

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Preserving AUC Fairness in Learning with Noisy Protected Groups and Chen, G

Reference 24

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Preserving AUC Fairness in Learning with Noisy Protected Groups Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels

Reference 25

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Preserving AUC Fairness in Learning with Noisy Protected Groups H., and Lyu, S

Reference 26

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Preserving AUC Fairness in Learning with Noisy Protected Groups and Zhou, A

Reference 27

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This paper cites Assessing algorithmic fairness with unobserved protected class using data combination.

Preserving AUC Fairness in Learning with Noisy Protected Groups Assessing algorithmic fairness with unobserved protected class using data combination

Reference 28

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Preserving AUC Fairness in Learning with Noisy Protected Groups J., Kahou, S

Reference 29

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This paper cites Domain adaptation explainability & fairness in ai for medical image analysis: Diagnosis of covid-19 based on 3-d chest ct-scans.

Preserving AUC Fairness in Learning with Noisy Protected Groups Domain adaptation explainability & fairness in ai for medical image analysis: Diagnosis of covid-19 based on 3-d chest ct-scans

Reference 30

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This paper cites Determinants of social desirability bias in sensitive surveys: a literature review.

Preserving AUC Fairness in Learning with Noisy Protected Groups Determinants of social desirability bias in sensitive surveys: a literature review

Reference 31

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Preserving AUC Fairness in Learning with Noisy Protected Groups Auc maximization under positive distribution shift

Reference 32

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Preserving AUC Fairness in Learning with Noisy Protected Groups Fairness without demographics through adversarially reweighted learning

Reference 33

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Preserving AUC Fairness in Learning with Noisy Protected Groups C., and Sidford, A

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Preserving AUC Fairness in Learning with Noisy Protected Groups Multimodal foundation models: From specialists to general-purpose assistants

Reference 35

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Preserving AUC Fairness in Learning with Noisy Protected Groups Celeb-df: A new dataset for deepfake forensics

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Preserving AUC Fairness in Learning with Noisy Protected Groups Preserving fairness generalization in deepfake detection

Reference 37

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Preserving AUC Fairness in Learning with Noisy Protected Groups Ai-face: A million-scale demographically annotated ai-generated face dataset and fairness benchmark

Reference 38

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Preserving AUC Fairness in Learning with Noisy Protected Groups and Vishnoi, N

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Preserving AUC Fairness in Learning with Noisy Protected Groups Pairwise fairness for ranking and regression

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Preserving AUC Fairness in Learning with Noisy Protected Groups Learning a deep dual-level network for robust deepfake detection

Reference 41

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Preserving AUC Fairness in Learning with Noisy Protected Groups W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

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Preserving AUC Fairness in Learning with Noisy Protected Groups Justice as fairness: A restatement

Reference 43

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Preserving AUC Fairness in Learning with Noisy Protected Groups T., Uryasev, S., et al

Reference 44

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Preserving AUC Fairness in Learning with Noisy Protected Groups Faceforensics++: Learning to detect manipulated facial images

Reference 45

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Preserving AUC Fairness in Learning with Noisy Protected Groups and Le, Q

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Preserving AUC Fairness in Learning with Noisy Protected Groups M., Huang, H., Khan, M

Reference 47

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Preserving AUC Fairness in Learning with Noisy Protected Groups Learning fair scoring functions: Bipartite ranking under roc-based fairness constraints

Reference 48

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This paper cites Robust optimization for fairness with noisy protected groups.

Preserving AUC Fairness in Learning with Noisy Protected Groups Robust optimization for fairness with noisy protected groups

Reference 49

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This paper cites Vision-language models are strong noisy label detectors.

Preserving AUC Fairness in Learning with Noisy Protected Groups Vision-language models are strong noisy label detectors

Reference 50

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Preserving AUC Fairness in Learning with Noisy Protected Groups and Menon, A

Reference 51

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This paper cites Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities.

Preserving AUC Fairness in Learning with Noisy Protected Groups Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities

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Preserving AUC Fairness in Learning with Noisy Protected Groups Algorithmic Foundations of Empirical X-risk Minimization

Reference 53

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This paper cites L., Varshney, K.

Preserving AUC Fairness in Learning with Noisy Protected Groups L., Varshney, K

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:50.360067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c377d64-87f8-4a02-b813-9024f7c9f47c · outbound

This paper cites Stochastic methods for auc optimization subject to auc-based fairness constraints.

Preserving AUC Fairness in Learning with Noisy Protected Groups Stochastic methods for auc optimization subject to auc-based fairness constraints

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:50.191954Z

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Observation bc414d89-9254-43f9-a53c-472eab1aa379 · outbound

This paper cites and Lien, C.-h.

Preserving AUC Fairness in Learning with Noisy Protected Groups and Lien, C.-h

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:50.090171Z

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Observation d686f1be-2307-4986-a02c-e035ace87863 · outbound

This paper cites How does disagreement help generalization against label corruption? In International conference on machine learning, pp.\ 7164--7173.

Preserving AUC Fairness in Learning with Noisy Protected Groups How does disagreement help generalization against label corruption? In International conference on machine learning, pp.\ 7164--7173

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:49.905492Z

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Observation 3eb0f4ad-554c-4768-a012-7ea927194243 · outbound

This paper cites Large-scale robust deep auc maximization: A new surrogate loss and empirical studies on medical image classification.

Preserving AUC Fairness in Learning with Noisy Protected Groups Large-scale robust deep auc maximization: A new surrogate loss and empirical studies on medical image classification

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:49.748294Z

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Observation dd5de689-eaed-4431-98d5-d045889b5505 · outbound

This paper cites Doubly robust auc optimization against noisy and adversarial samples.

Preserving AUC Fairness in Learning with Noisy Protected Groups Doubly robust auc optimization against noisy and adversarial samples

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:49.621006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation a8152a37-8a0e-4b1b-aba8-9631095e3aaf · inbound

Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study cites this paper.

Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study Preserving AUC Fairness in Learning with Noisy Protected Groups

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-01T05:25:16.744754Z

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