Paper Citation Record · LEDGER

Risk-averse Fair Multi-class Classification

As of 21 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2509.05771.

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

pith.paper-citation-record.v1
2509.05771 v2

Coverage vector

measured 42 of 42 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-05T05:06:36.957281Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

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Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 32ecafd7-38fb-4988-8f66-5b7c6cc98fce · outbound

This paper cites On risk evaluation and control of distributed multi-agent systems.Journal of Optimization Theory and Applications, pages 1–30, 2024.

Risk-averse Fair Multi-class Classification On risk evaluation and control of distributed multi-agent systems.Journal of Optimization Theory and Applications, pages 1–30, 2024

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.315210Z

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Observation a8aecdfb-8ee0-4ab2-9233-ab1d869ca3bf · outbound

This paper cites Coherent measures of risk.Mathematical finance, 9(3):203–228, 1999.

Risk-averse Fair Multi-class Classification Coherent measures of risk.Mathematical finance, 9(3):203–228, 1999

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.306055Z

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Observation 84ed7208-e2dc-4726-9c7e-7c6ea9131c4d · outbound

This paper cites A general and adaptive robust loss function.

Risk-averse Fair Multi-class Classification A general and adaptive robust loss function

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.297185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d67c99e1-ec67-4153-96c7-eb2e302209d2 · outbound

This paper cites Ben-Tal, S.

Risk-averse Fair Multi-class Classification Ben-Tal, S

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.288494Z

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Observation d3196331-94f2-4c19-8fab-99e390f0fda7 · outbound

This paper cites Robust classification.Journal on Optimization, 1:2–34, 2018.

Risk-averse Fair Multi-class Classification Robust classification.Journal on Optimization, 1:2–34, 2018

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.280338Z

Source-reported events for the cited work

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Observation 89024f40-b037-4c22-9e7a-1b1c9beac5ef · outbound

This paper cites Support vector classification with input data uncertainty.

Risk-averse Fair Multi-class Classification Support vector classification with input data uncertainty

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.272032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 940f4f4b-a6fe-48ec-8fd7-daf92c73e0ca · outbound

This paper cites Random forests.Machine learning, 45:5–32, 2001.

Risk-averse Fair Multi-class Classification Random forests.Machine learning, 45:5–32, 2001

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T05:06:36.863677Z

Source-reported events for the cited work

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Observation 93ded4c2-06bc-4093-bc34-244b66ffa1e5 · outbound

This paper cites Fair regression with wasserstein barycenters.Advances in Neural Information Processing Systems, 33:7321–7331, 2020.

Risk-averse Fair Multi-class Classification Fair regression with wasserstein barycenters.Advances in Neural Information Processing Systems, 33:7321–7331, 2020

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.258604Z

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Observation b7eef254-89f2-4858-9947-09f76d5ade4d · outbound

This paper cites Crammer and Y.

Risk-averse Fair Multi-class Classification Crammer and Y

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.250897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 867fc742-58b4-4681-ba31-24cfb5441908 · outbound

This paper cites Coherent risk measures on general probability spaces.Advances in Finance and Stochastics, pages 1–37, March 2000.

Risk-averse Fair Multi-class Classification Coherent risk measures on general probability spaces.Advances in Finance and Stochastics, pages 1–37, March 2000

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.243621Z

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Observation 0ce59a24-7c42-4cb5-b8fe-fefe7d35c64b · outbound

This paper cites Springer Series in Operations Research and Financial Engineering.

Risk-averse Fair Multi-class Classification Springer Series in Operations Research and Financial Engineering

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.235808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 43f4e70a-5939-44bc-b677-f6e32e169e0b · outbound

This paper cites Empirical risk minimization under fairness constraints.Advances in neural information processing systems, 31, 2018.

Risk-averse Fair Multi-class Classification Empirical risk minimization under fairness constraints.Advances in neural information processing systems, 31, 2018

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.227033Z

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Observation 7eb33f97-d029-4824-8f17-b0dd8c09d8a1 · outbound

This paper cites UCI machine learning repository, 2019.

Risk-averse Fair Multi-class Classification UCI machine learning repository, 2019

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.217719Z

Source-reported events for the cited work

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Observation d345dcc0-6c58-4bd3-861b-9f70bda25f96 · outbound

This paper cites Electrical fault detection and classification.

Risk-averse Fair Multi-class Classification Electrical fault detection and classification

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.209685Z

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Observation 69610d71-fcfa-414f-a7fb-1239aef2ee49 · outbound

This paper cites Cer- tifying and removing disparate impact.

Risk-averse Fair Multi-class Classification Cer- tifying and removing disparate impact

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.201270Z

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Observation 844e3fa5-d390-4af4-9c1f-e82195da24db · outbound

This paper cites A confidence-based approach for balancing fairness and accuracy.

Risk-averse Fair Multi-class Classification A confidence-based approach for balancing fairness and accuracy

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.192957Z

Source-reported events for the cited work

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Observation 79b837f9-3918-4f10-bb75-a07554f6366c · outbound

This paper cites Walter De Gruyter, 2011.

Risk-averse Fair Multi-class Classification Walter De Gruyter, 2011

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.184471Z

Source-reported events for the cited work

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Observation 84d253b5-f44e-4efa-a13e-62b504463c44 · outbound

This paper cites A decision-theoretic generalization of on-line learning and an application to boosting.Journal of computer and system sciences, 55(1):119–139, 1997.

Risk-averse Fair Multi-class Classification A decision-theoretic generalization of on-line learning and an application to boosting.Journal of computer and system sciences, 55(1):119–139, 1997

Reference 18

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Observation b8426960-fe3d-49f2-b018-dfb9b5f8e0f6 · outbound

This paper cites Greedy function approximation: a gradient boosting machine.Annals of statistics, pages 1189–1232, 2001.

Risk-averse Fair Multi-class Classification Greedy function approximation: a gradient boosting machine.Annals of statistics, pages 1189–1232, 2001

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T05:06:36.897371Z

Source-reported events for the cited work

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Observation 838542d5-6704-456b-a054-74394fb55c61 · outbound

This paper cites Ghaoui and H.

Risk-averse Fair Multi-class Classification Ghaoui and H

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.165933Z

Source-reported events for the cited work

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Observation ec4fe66f-ebda-4ab2-aa38-5753d6a01781 · outbound

This paper cites Robust loss functions under label noise for deep neural networks.

Risk-averse Fair Multi-class Classification Robust loss functions under label noise for deep neural networks

Reference 21

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Source-reported events for the cited work

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Observation 6bdeb898-032e-4089-9a51-565082c4bed3 · outbound

This paper cites Support vector machines based on convex risk functions and general norms.

Risk-averse Fair Multi-class Classification Support vector machines based on convex risk functions and general norms

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.152145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 481c4921-7bdd-4b02-9146-7f08622d1078 · outbound

This paper cites Two-stage portfolio optimization with higher-order conditional measures of risk.Annals of Operations Research, 229:409–427, 2015.

Risk-averse Fair Multi-class Classification Two-stage portfolio optimization with higher-order conditional measures of risk.Annals of Operations Research, 229:409–427, 2015

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.144044Z

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Observation 31ce9f2c-6897-4b57-a371-b8167bc4253e · outbound

This paper cites Data preprocessing techniques for classification without discrimination.

Risk-averse Fair Multi-class Classification Data preprocessing techniques for classification without discrimination

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.134609Z

Source-reported events for the cited work

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Observation bd302e32-12ba-40cc-925e-1d349a3952b9 · outbound

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Risk-averse Fair Multi-class Classification Unresolved cited work

Reference 25

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Observation db4842a4-7edf-4ca9-b639-af80ee183d8e · outbound

This paper cites Robust classification via mom minimization.Ma- chine learning, 109:1635–1665, 2020.

Risk-averse Fair Multi-class Classification Robust classification via mom minimization.Ma- chine learning, 109:1635–1665, 2020

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.117579Z

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Observation 62d5e168-671c-4294-aeea-cb8b1758b756 · outbound

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Risk-averse Fair Multi-class Classification Unresolved cited work

Reference 27

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Observation 54e92e9c-13cf-4c16-b70f-bc1b38d1351d · outbound

This paper cites Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels.

Risk-averse Fair Multi-class Classification Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

Reference 28

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This paper cites Norton, A.

Risk-averse Fair Multi-class Classification Norton, A

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.100881Z

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Observation 79c083a2-5a12-49e9-951b-4a013786b3a0 · outbound

This paper cites Making deep neural networks robust to label noise: A loss correction approach.

Risk-averse Fair Multi-class Classification Making deep neural networks robust to label noise: A loss correction approach

Reference 30

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This paper cites Pflug and W.

Risk-averse Fair Multi-class Classification Pflug and W

Reference 31

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Observation 6d8dd481-396d-4252-976d-2d088259026b · outbound

This paper cites On fairness and calibration.

Risk-averse Fair Multi-class Classification On fairness and calibration

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.079023Z

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Observation ade9b1d9-97c0-4902-b2ec-7e924dae7103 · outbound

This paper cites FairBatch: Batch Selection for Model Fairness.

Risk-averse Fair Multi-class Classification FairBatch: Batch Selection for Model Fairness

Reference 33

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This paper cites Ruszczy ´nski and A.

Risk-averse Fair Multi-class Classification Ruszczy ´nski and A

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.069894Z

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Observation bb67ce5b-0bfc-4c33-ba43-6892f3029cf7 · outbound

This paper cites Ruszczy ´nski and A.

Risk-averse Fair Multi-class Classification Ruszczy ´nski and A

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.061450Z

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Observation 29bb9c4a-b3b9-423f-8cc3-c93d792f8e07 · outbound

This paper cites A regularized decomposition method for minimizing a sum of polyhedral functions.

Risk-averse Fair Multi-class Classification A regularized decomposition method for minimizing a sum of polyhedral functions

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.052330Z

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Observation 8ecfc9c8-9650-4809-b5aa-a0d596906505 · outbound

This paper cites Metrizing Fairness.

Risk-averse Fair Multi-class Classification Metrizing Fairness

Reference 37

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Observation a528f83e-3388-4749-a9b1-54d9a27a0503 · outbound

This paper cites Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations.

Risk-averse Fair Multi-class Classification Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.043331Z

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Observation 21b89e7d-292d-4124-9001-6c9f3b7e0fb1 · outbound

This paper cites A tun- able loss function for robust classification: Calibration, landscape, and generalization.IEEE Transactions on Information Theory, 68(9):6021–6051, 2022.

Risk-averse Fair Multi-class Classification A tun- able loss function for robust classification: Calibration, landscape, and generalization.IEEE Transactions on Information Theory, 68(9):6021–6051, 2022

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.034834Z

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Observation 4f628318-0e52-49e3-b1bd-e7110edf10b9 · outbound

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Risk-averse Fair Multi-class Classification Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-05T05:06:37.025420Z

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Observation 0176f2e7-efe1-41d2-9767-82923a6659e7 · outbound

This paper cites Weston and C.

Risk-averse Fair Multi-class Classification Weston and C

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.017195Z

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Observation 6c6a0148-6a69-4618-9809-697287987560 · outbound

This paper cites Learning fair representations.

Risk-averse Fair Multi-class Classification Learning fair representations

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:06:37.009255Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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