Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective | Paper Citation Record

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This paper cites A reductions approach to fair classification.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective A reductions approach to fair classification

Reference 1

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This paper cites Towards a unified theory of learning and information.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Towards a unified theory of learning and information

Reference 2

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This paper cites Beyond adult and compas: Fair multi-class prediction via information projection.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Beyond adult and compas: Fair multi-class prediction via information projection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.437924Z

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This paper cites An exact characterization of the generalization error for the gibbs algorithm.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective An exact characterization of the generalization error for the gibbs algorithm

Reference 4

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This paper cites R 'enyi fair inference.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective R 'enyi fair inference

Reference 5

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This paper cites Fairness and machine learning.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Fairness and machine learning

Reference 6

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This paper cites and Mukherjee, S.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective and Mukherjee, S

Reference 7

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This paper cites Concentration Inequalities: A Nonasymptotic Theory of Independence.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Concentration Inequalities: A Nonasymptotic Theory of Independence

Reference 8

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Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Unresolved cited work

Reference 9

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Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Unresolved cited work

Reference 10

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This paper cites Generalization bounds for meta-learning: An information-theoretic analysis.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Generalization bounds for meta-learning: An information-theoretic analysis

Reference 11

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This paper cites Fairness transferability subject to bounded distribution shift.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Fairness transferability subject to bounded distribution shift

Reference 12

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This paper cites Fair prediction with disparate impact: A study of bias in recidivism prediction instruments.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Fair prediction with disparate impact: A study of bias in recidivism prediction instruments

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.320916Z

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This paper cites Algorithmic decision making and the cost of fairness.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Algorithmic decision making and the cost of fairness

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.308138Z

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This paper cites N., Wei, D., Varshney, K.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective N., Wei, D., Varshney, K

Reference 15

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Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Unresolved cited work

Reference 16

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This paper cites Fairness guarantee in multi-class classification.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Fairness guarantee in multi-class classification

Reference 17

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This paper cites Towards generalization beyond pointwise learning: A unified information-theoretic perspective.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Towards generalization beyond pointwise learning: A unified information-theoretic perspective

Reference 18

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Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Unresolved cited work

Reference 19

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This paper cites Fairness through awareness.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Fairness through awareness

Reference 20

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This paper cites Estimating mutual information for discrete-continuous mixtures.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Estimating mutual information for discrete-continuous mixtures

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.229457Z

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This paper cites C., Thomas, P.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective C., Thomas, P

Reference 22

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This paper cites Learning fair representations via distance correlation minimization.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Learning fair representations via distance correlation minimization

Reference 23

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This paper cites Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation

Reference 24

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This paper cites Ffb: A fair fairness benchmark for in-processing group fairness methods.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Ffb: A fair fairness benchmark for in-processing group fairness methods

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.189539Z

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This paper cites Equality of opportunity in supervised learning.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Equality of opportunity in supervised learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.176369Z

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This paper cites Information-theoretic generalization bounds for black-box learning algorithms.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Information-theoretic generalization bounds for black-box learning algorithms

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.163449Z

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This paper cites Nearly-tight vc-dimension bounds for piecewise linear neural networks.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Nearly-tight vc-dimension bounds for piecewise linear neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.149655Z

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This paper cites and Durisi, G.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective and Durisi, G

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.136401Z

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This paper cites and Liu, H.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective and Liu, H

Reference 30

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This paper cites Wasserstein fair classification.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Wasserstein fair classification

Reference 31

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Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Unresolved cited work

Reference 32

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Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective and Calders, T

Reference 33

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This paper cites Fairness-aware classifier with prejudice remover regularizer.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Fairness-aware classifier with prejudice remover regularizer

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.085594Z

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This paper cites Inherent Trade-Offs in the Fair Determination of Risk Scores.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Inherent Trade-Offs in the Fair Determination of Risk Scores

Reference 35

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Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective and Becker, B

Reference 36

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This paper cites Estimating mutual information.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Estimating mutual information

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.060290Z

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Observation 8b282bce-84c0-402c-a0a4-2cf895af7b75 · outbound

This paper cites Class-wise Generalization Error: an Information-Theoretic Analysis.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Class-wise Generalization Error: an Information-Theoretic Analysis

Reference 38

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This paper cites Propublica compas analysis—data and analysis for ‘machine bias.’.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Propublica compas analysis—data and analysis for ‘machine bias.’

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.047513Z

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This paper cites A maximal correlation approach to imposing fairness in machine learning.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective A maximal correlation approach to imposing fairness in machine learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.034723Z

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This paper cites K., Bu, Y., Rajan, D., Sattigeri, P., Panda, R., Das, S., and Wornell, G.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective K., Bu, Y., Rajan, D., Sattigeri, P., Panda, R., Das, S., and Wornell, G

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.021273Z

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This paper cites and Liu, H.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective and Liu, H

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:08.008644Z

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This paper cites Kernel dependence regularizers and gaussian processes with applications to algorithmic fairness.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Kernel dependence regularizers and gaussian processes with applications to algorithmic fairness

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.995534Z

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This paper cites Learning Adversarially Fair and Transferable Representations.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Learning Adversarially Fair and Transferable Representations

Reference 44

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Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective A survey on bias and fairness in machine learning

Reference 45

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This paper cites and Vishnoi, N.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective and Vishnoi, N

Reference 46

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Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Unresolved cited work

Reference 47

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Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Unresolved cited work

Reference 48

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This paper cites K., Haghifam, M., and Roy, D.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective K., Haghifam, M., and Roy, D

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.929571Z

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This paper cites Learning fair and transferable representations with theoretical guarantees.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Learning fair and transferable representations with theoretical guarantees

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.916367Z

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This paper cites Randomized learning and generalization of fair and private classifiers: From pac-bayes to stability and differential privacy.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Randomized learning and generalization of fair and private classifiers: From pac-bayes to stability and differential privacy

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.903680Z

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Observation 4d28b3ca-86bc-4390-8bda-21c4df5ba070 · outbound

This paper cites and Shmueli, E.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective and Shmueli, E

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.890699Z

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This paper cites Fairness and accuracy under domain generalization.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Fairness and accuracy under domain generalization

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.878080Z

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Observation d5bb5fc0-9bc9-49ff-9502-231d86a7ae28 · outbound

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Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:34:07.865011Z

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Observation 299ef964-3a88-4b49-8b77-9539834ed3b5 · outbound

This paper cites T., Durisi, G., and Simeone, O.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective T., Durisi, G., and Simeone, O

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.852182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fb3a6599-d56f-4beb-967f-0b6312e6b056 · outbound

This paper cites Tighter expected generalization error bounds via wasserstein distance.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Tighter expected generalization error bounds via wasserstein distance

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.839188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dca845e4-bd8e-4d42-aa09-30deee395d8a · outbound

This paper cites an unresolved cited work.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:34:07.826225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 05c85676-095f-491b-83e6-d2d551cc256d · outbound

This paper cites M., Pugnana, A., Turini, F., et al.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective M., Pugnana, A., Turini, F., et al

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.813713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0f4b0f5a-bad9-4fed-ab47-d67dc24102e1 · outbound

This paper cites Transfer of Machine Learning Fairness across Domains.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Transfer of Machine Learning Fairness across Domains

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:34:07.315379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7b83fdf7-80b0-41eb-b5a1-6eff16243c8a · outbound

This paper cites K., Das, S., Panda, R., Sattigeri, P., and Wornell, G.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective K., Das, S., Panda, R., Sattigeri, P., and Wornell, G

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.801308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ee3f4243-5f33-43ce-ae2b-f40d1a485035 · outbound

This paper cites Average individual fairness: Algorithms, generalization and experiments.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Average individual fairness: Algorithms, generalization and experiments

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.788045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5395e111-6261-4c4b-98a1-b704362b7d7f · outbound

This paper cites Beyond $\mathcal{H}$-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Beyond $\mathcal{H}$-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:34:07.296344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9e5f349e-279e-405c-9aee-755944982898 · outbound

This paper cites X., Arbel, T., Wang, B., and Gagn \'e , C.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective X., Arbel, T., Wang, B., and Gagn \'e , C

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.774620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1428c9e7-4a9d-4a44-a39a-949d8573f0d1 · outbound

This paper cites Fairness violations and mitigation under covariate shift.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Fairness violations and mitigation under covariate shift

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.761500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 78306ead-7542-4f6b-9bb3-1b97aee12324 · outbound

This paper cites an unresolved cited work.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:34:07.748345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cc08b785-b684-42d9-8a23-0b7a84603206 · outbound

This paper cites and Zakynthinou, L.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective and Zakynthinou, L

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.734490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a5a48f48-fd50-4f4e-a71a-75c5608016cc · outbound

This paper cites and Zhang, K.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective and Zhang, K

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.720173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4306e78f-d98f-4082-a6ca-5ce344ed0fff · outbound

This paper cites an unresolved cited work.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:34:07.706446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 26b62803-5ad4-47d4-b176-711a628ac94e · outbound

This paper cites On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:34:07.277171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dc115f9a-e853-41b8-b659-a7211a7bbcde · outbound

This paper cites Tighter Information-Theoretic Generalization Bounds from Supersamples.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Tighter Information-Theoretic Generalization Bounds from Supersamples

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:34:07.257647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 29b2360f-ea53-4033-a3a5-1a4fca4a7b11 · outbound

This paper cites I., and Srebro, N.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective I., and Srebro, N

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.693378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aca4c720-69a3-4a2e-b3d7-66f890ede65b · outbound

This paper cites H., Aickelin, U., and Zhu, J.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective H., Aickelin, U., and Zhu, J

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.679049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5c43352a-3827-4f6e-8650-b1e13a2e616a · outbound

This paper cites H., Aickelin, U., and Zhu, J.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective H., Aickelin, U., and Zhu, J

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.664636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation acb13403-24ed-4de2-a00d-91ff52c18ce5 · outbound

This paper cites and Raginsky, M.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective and Raginsky, M

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:07.168601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21662b4a-129d-4eb1-9215-08e60b1b6eb1 · outbound

This paper cites Joint transfer of model knowledge and fairness over domains using wasserstein distance.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Joint transfer of model knowledge and fairness over domains using wasserstein distance

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.642463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ee0f74ce-6425-4070-a8d2-ffa40edb8c85 · outbound

This paper cites B., Valera, I., Rogriguez, M.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective B., Valera, I., Rogriguez, M

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.628631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 37464eb4-6783-49a5-8c55-387f0ddd1a73 · outbound

This paper cites Learning fair representations.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Learning fair representations

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.615259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8ded4634-5a3c-4e39-ab43-ce8db658a1c0 · outbound

This paper cites Individually conditional individual mutual information bound on generalization error.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Individually conditional individual mutual information bound on generalization error

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:07.600609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3b6248b8-4172-47b3-a803-c2401ed9670f · outbound

This paper cites Exactly Tight Information-Theoretic Generalization Error Bound for the Quadratic Gaussian Problem.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Exactly Tight Information-Theoretic Generalization Error Bound for the Quadratic Gaussian Problem

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:34:07.237565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 07d14fd9-db4a-4f96-a6b7-3092e799ed0f · outbound

This paper cites write newline.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective write newline

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:07.193449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers