Paper Citation Record · LEDGER

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering

As of 8 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2607.18119.

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pith.paper-citation-record.v1
2607.18119 v1

Coverage vector

measured 46 of 46 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-01T16:10:33.576093Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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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

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation e96c2246-11ed-454a-9092-8f0c2863c6a5 · outbound

This paper cites A reductions approach to fair classification.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering A reductions approach to fair classification

Reference 1

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Observation 7fc0c795-4e49-4d67-9c0b-88c99ca761f7 · outbound

This paper cites Fair regression: Quantitative definitions and reduction-based algorithms.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Fair regression: Quantitative definitions and reduction-based algorithms

Reference 2

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Observation 008fa6f7-e2ce-487c-a322-437f8b1e8624 · outbound

This paper cites Scalable fair clustering.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Scalable fair clustering

Reference 3

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Observation d2342436-61dd-4852-a96c-23cd0ae8c1be · outbound

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Unresolved cited work

Reference 4

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Observation 06ff4b92-b273-403b-b8ff-7007ef26ba8f · outbound

This paper cites Fair Algorithms for Clustering.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Fair Algorithms for Clustering

Reference 5

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Observation 9edfc883-0372-4703-b8e6-7be6209e6d0e · outbound

This paper cites Farias, and Nikolaos Trichakis.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Farias, and Nikolaos Trichakis

Reference 6

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Observation 01d2b2fc-135e-4a1e-92c3-ff1e1dbd90c4 · outbound

This paper cites Advancing Subgroup Fairness via Sleeping Experts.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Advancing Subgroup Fairness via Sleeping Experts

Reference 7

Resolution: verified exact; doi, observed 2026-08-01T16:14:07.302360Z

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Observation 7c567610-44c4-4135-b6da-7e5a365a6899 · outbound

This paper cites Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification

Reference 8

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Observation 829c84d1-af69-49b4-a3f2-de7e5c623082 · outbound

This paper cites An overview of fairness in clustering.IEEE Access, 9:130698–130720, 2021.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering An overview of fairness in clustering.IEEE Access, 9:130698–130720, 2021

Reference 9

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Observation 9d4e39c4-4c3b-41f6-8340-1ba8907324c2 · outbound

This paper cites Fair clustering through fairlets.Advances in neural information processing systems, 30, 2017.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Fair clustering through fairlets.Advances in neural information processing systems, 30, 2017

Reference 10

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Observation 0d59adb2-023a-4ece-8aa0-0c0406e1aad2 · outbound

This paper cites Fair regression with wasserstein barycenters.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Fair regression with wasserstein barycenters

Reference 11

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Observation 817beed2-1ba9-4c4c-9634-15fce924c531 · outbound

This paper cites Retiring adult: new datasets for fair machine learning.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Retiring adult: new datasets for fair machine learning

Reference 12

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Observation 259adcc4-00ff-460d-bed2-30fc5ccace97 · outbound

This paper cites Empirical risk minimization under fairness constraints.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Empirical risk minimization under fairness constraints

Reference 13

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Observation c9e66cb5-fd72-4370-acf8-2400eba9da4f · outbound

This paper cites Fair clustering under a bounded cost.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Fair clustering under a bounded cost

Reference 14

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Observation aa55a3c3-29fa-4bc4-b518-d8281c07e048 · outbound

This paper cites Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness

Reference 15

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Observation fb0c92f3-5196-4c64-b148-af7e82fb03aa · outbound

This paper cites Satisfying real-world goals with dataset constraints.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Satisfying real-world goals with dataset constraints

Reference 16

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Observation b9f5af82-4b00-47ea-93bb-010ab46e71ad · outbound

This paper cites right to explanation.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering right to explanation

Reference 17

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Observation ed9d64f5-db43-4102-9e5c-a32072e52fe8 · outbound

This paper cites Deep residual learning for image recognition.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Deep residual learning for image recognition

Reference 18

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Observation 6ee4feaf-8252-43a9-925c-e97a48043883 · outbound

This paper cites A sequentially fair mechanism for multiple sensitive attributes.Proceedings of the AAAI Conference on Artificial Intelligence, 38(11): 12502–12510, March 2024.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering A sequentially fair mechanism for multiple sensitive attributes.Proceedings of the AAAI Conference on Artificial Intelligence, 38(11): 12502–12510, March 2024

Reference 19

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Observation 03c9e79b-dce6-42ac-adc2-c1ccf643760b · outbound

This paper cites Clustering with size constraints.Studies in Computational Intelligence, 137, 01 2008.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Clustering with size constraints.Studies in Computational Intelligence, 137, 01 2008

Reference 20

Resolution: verified exact; doi, observed 2026-08-01T16:14:06.845999Z

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Observation 4ef26f2c-1801-42f4-a785-b0e4125e1558 · outbound

This paper cites An empirical study of rich subgroup fairness for machine learning, 2018.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering An empirical study of rich subgroup fairness for machine learning, 2018

Reference 21

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Observation 7a3ea090-3e91-4df6-be25-ec030bbc2efb · outbound

This paper cites Preventing fairness gerryman- dering: Auditing and learning for subgroup fairness.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Preventing fairness gerryman- dering: Auditing and learning for subgroup fairness

Reference 22

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Observation 7d7c4d16-7b19-4d2b-8dd2-35c5f42c3607 · outbound

This paper cites Slide: A surrogate fairness constraint to ensure fairness consistency.Neural Networks, 154:441–454, 2022.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Slide: A surrogate fairness constraint to ensure fairness consistency.Neural Networks, 154:441–454, 2022

Reference 23

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Observation a4dd8a6a-93bc-4472-bdcd-4365af9119ae · outbound

This paper cites Fairness through matching.Transactions on Machine Learning Research, 2025.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Fairness through matching.Transactions on Machine Learning Research, 2025

Reference 24

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Observation a3c0c746-277f-4c6d-9880-80e7315b1b4b · outbound

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Fair clustering via align- ment

Reference 25

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Observation bbfbc650-30d6-49d4-86d4-f953cade0e1e · outbound

This paper cites Doubly- regressing approach for subgroup fairness, 2026.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Doubly- regressing approach for subgroup fairness, 2026

Reference 26

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Observation 1a1b97a7-6f7a-4fda-a1ec-746d82da75ea · outbound

This paper cites Guarantees for spectral clustering with fairness constraints.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Guarantees for spectral clustering with fairness constraints

Reference 27

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Observation b0d6545a-cb2d-4fb9-a0c5-a84415d67d54 · outbound

This paper cites FairICP: Encouraging equalized odds via inverse condi- tional permutation.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering FairICP: Encouraging equalized odds via inverse condi- tional permutation

Reference 28

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Observation 8a896fa9-dba7-4e8c-a6e8-3d224a62dd29 · outbound

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models

Reference 29

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Deep fair clustering for visual learning

Reference 30

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Deep learning face attributes in the wild

Reference 31

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Observation 4394ab10-535d-43d7-884f-abea65c39111 · outbound

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Unresolved cited work

Reference 32

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This paper cites A survey on bias and fairness in machine learning.ACM computing surveys (CSUR), 54(6):1–35, 2021.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering A survey on bias and fairness in machine learning.ACM computing surveys (CSUR), 54(6):1–35, 2021

Reference 33

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This paper cites Bounding and Approximating Intersectional Fairness through Marginal Fairness.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Bounding and Approximating Intersectional Fairness through Marginal Fairness

Reference 34

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This paper cites A data-driven approach to predict the success of bank telemarketing.Decision Support Systems, 62:22–31, 2014.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering A data-driven approach to predict the success of bank telemarketing.Decision Support Systems, 62:22–31, 2014

Reference 35

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Observation 4454574e-32cd-49ee-97c6-28f3e4f5a1ad · outbound

This paper cites A data-driven software tool for enabling cooperative information sharing among police departments.European Journal of Operational Research, 141(3):660–678, 2002.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering A data-driven software tool for enabling cooperative information sharing among police departments.European Journal of Operational Research, 141(3):660–678, 2002

Reference 36

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Sundaram

Reference 37

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application

Reference 38

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Fairgan: Fairness-aware generative adversarial networks

Reference 39

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Deep fair clustering via maximizing and minimizing mutual information: Theory, algorithm and metric

Reference 40

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This paper cites Data clustering with size constraints.Knowledge- Based Systems, 23(8):883–889, 2010.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Data clustering with size constraints.Knowledge- Based Systems, 23(8):883–889, 2010

Reference 41

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Observation 3830b297-06f4-4266-8df9-3a087826908a · outbound

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Variational fair clustering

Reference 42

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering # subgroups per attr

Reference 44

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COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Limitations

Reference 45

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Observation 6029b75c-1fcf-4d25-98ed-fbddae6e3342 · outbound

This paper cites Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects.

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects

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Reference 8856

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