Corruptions of Supervised Learning Problems: Typology and Mitigations | Paper Citation Record

Observation 96d35cc1-39ec-4928-ad98-f5cdcefe9cc3 · outbound

This paper cites University of Chicago Press.

Corruptions of Supervised Learning Problems: Typology and Mitigations University of Chicago Press

Reference 1

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Observation 96d35ceb-5e20-4af6-b353-8026284b2d37 · outbound

This paper cites ProcessandPurpose,NotThingandTechnique: HowtoPoseData Science Research Challenges.Harvard Data Science Review, 2(3).

Corruptions of Supervised Learning Problems: Typology and Mitigations ProcessandPurpose,NotThingandTechnique: HowtoPoseData Science Research Challenges.Harvard Data Science Review, 2(3)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.134980Z

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Observation 6afe1739-009f-4fd5-8a58-51c7d75505ea · outbound

This paper cites How to prevent discriminatory outcomes in machine learning.

Corruptions of Supervised Learning Problems: Typology and Mitigations How to prevent discriminatory outcomes in machine learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.128068Z

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Observation d29c8b5c-568f-4fd3-b32f-620dcbd0f252 · outbound

This paper cites Shifts: A dataset of real distributional shift across multiple large-scale tasks.

Corruptions of Supervised Learning Problems: Typology and Mitigations Shifts: A dataset of real distributional shift across multiple large-scale tasks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.914831Z

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Observation 6b092608-b20a-413b-ab0f-7bde091da8f8 · outbound

This paper cites Wilds: A benchmark of in-the-wild distribution shifts.

Corruptions of Supervised Learning Problems: Typology and Mitigations Wilds: A benchmark of in-the-wild distribution shifts

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.110212Z

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Observation 9acb8b40-2fc3-4a62-9e03-37cb5bf51b16 · outbound

This paper cites Enhancing(publicationson)dataquality: Deeperdatamindingandfuller data confession.Journal of the Royal Statistical Society Series A: Statistics in Society, 184(4): 1161–1175.

Corruptions of Supervised Learning Problems: Typology and Mitigations Enhancing(publicationson)dataquality: Deeperdatamindingandfuller data confession.Journal of the Royal Statistical Society Series A: Statistics in Society, 184(4): 1161–1175

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.962082Z

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Observation 93e43b17-6711-4ebb-a6ed-22b6e5101660 · outbound

This paper cites Thinking beyond distributions in testing machine learned models.

Corruptions of Supervised Learning Problems: Typology and Mitigations Thinking beyond distributions in testing machine learned models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.907507Z

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Observation 16afc5c2-bb11-44c5-bd90-9a00af5e7ffc · outbound

This paper cites Geometry and Stability of Supervised Learning Problems.

Corruptions of Supervised Learning Problems: Typology and Mitigations Geometry and Stability of Supervised Learning Problems

Reference 8

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Observation 5c344315-d8ce-472b-9df3-70df2e32aa68 · outbound

This paper cites Learning from noisy examples.Machine Learning, 2: 343–370.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning from noisy examples.Machine Learning, 2: 343–370

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.987768Z

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Observation 958bf064-a496-48d1-9fb2-a93070f7a0ba · outbound

This paper cites Domain adaptation under target and conditional shift.

Corruptions of Supervised Learning Problems: Typology and Mitigations Domain adaptation under target and conditional shift

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.911453Z

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Observation 9ccedaa4-9995-490a-beca-dd743687aafd · outbound

This paper cites Learning with noisy labels.Advances in neural information processing systems, 26.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning with noisy labels.Advances in neural information processing systems, 26

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.918916Z

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Observation ee48af61-edb0-480c-8967-03029db2a3dc · outbound

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

Corruptions of Supervised Learning Problems: Typology and Mitigations Making deep neural networks robust to label noise: A loss correction approach

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.904279Z

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Observation 07e8dcac-e6bb-4644-bf19-5e59686697a7 · outbound

This paper cites Improvingpredictiveinferenceundercovariateshiftbyweighting the log-likelihood function.Journal of statistical planning and inference, 90(2):227–244.

Corruptions of Supervised Learning Problems: Typology and Mitigations Improvingpredictiveinferenceundercovariateshiftbyweighting the log-likelihood function.Journal of statistical planning and inference, 90(2):227–244

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.984601Z

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Observation 6f76cef4-af85-45f3-ae88-d339759529c9 · outbound

This paper cites Dataset shift in machine learning.

Corruptions of Supervised Learning Problems: Typology and Mitigations Dataset shift in machine learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.852888Z

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Observation f2affe57-9a89-43be-b523-aadde159b463 · outbound

This paper cites A one-step approach to covariate shift adaptation.

Corruptions of Supervised Learning Problems: Typology and Mitigations A one-step approach to covariate shift adaptation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.084716Z

Source-reported events for the cited work

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Observation fac913c3-64c0-479b-a620-c902fd9cf771 · outbound

This paper cites A unifying view on dataset shift in classification.Pattern recognition, 45(1):521–530.

Corruptions of Supervised Learning Problems: Typology and Mitigations A unifying view on dataset shift in classification.Pattern recognition, 45(1):521–530

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.081472Z

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Observation a39e8662-bd88-49ee-96b8-c4a615ac581a · outbound

This paper cites Patterns of dataset shift.

Corruptions of Supervised Learning Problems: Typology and Mitigations Patterns of dataset shift

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.099246Z

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Observation 81e544e3-8bec-437e-a000-9b6e59259f07 · outbound

This paper cites an unresolved cited work.

Corruptions of Supervised Learning Problems: Typology and Mitigations Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-05-24T07:16:04.113595Z

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Observation 0bacc3c2-d9eb-4bdc-bbb9-58f8d262f02c · outbound

This paper cites A unifying causal framework for analyzing dataset shift-stable learning algorithms.Journal of Causal Inference, 10(1):64–89.

Corruptions of Supervised Learning Problems: Typology and Mitigations A unifying causal framework for analyzing dataset shift-stable learning algorithms.Journal of Causal Inference, 10(1):64–89

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.091413Z

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Observation a89e28eb-225d-46e0-94e2-56d9df540a23 · outbound

This paper cites Learning k-DNF with noise in the attributes.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning k-DNF with noise in the attributes

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.124539Z

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Observation d585a6a4-7645-49ea-8220-cd79a19d8924 · outbound

This paper cites Goldman and Robert H.

Corruptions of Supervised Learning Problems: Typology and Mitigations Goldman and Robert H

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.992087Z

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Observation 65305fbd-1da4-49ef-a68f-2fe603f32b96 · outbound

This paper cites Class noise vs.

Corruptions of Supervised Learning Problems: Typology and Mitigations Class noise vs

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.120978Z

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Observation c39ef726-e0c9-49d5-af47-ccc1ad963c54 · outbound

This paper cites Williamson and Zac Cranko.

Corruptions of Supervised Learning Problems: Typology and Mitigations Williamson and Zac Cranko

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.757293Z

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Observation 1c39c295-a34a-4b88-879e-33b84db1a788 · outbound

This paper cites Combining labeled and unlabeled data with co-training.

Corruptions of Supervised Learning Problems: Typology and Mitigations Combining labeled and unlabeled data with co-training

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.771530Z

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Observation 9478cb71-3815-4dd0-8659-0fe8fc7fe60a · outbound

This paper cites Learningwithsymmetric label noise: The importance of being unhinged.Advances in neural information processing systems, 28.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learningwithsymmetric label noise: The importance of being unhinged.Advances in neural information processing systems, 28

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.764688Z

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Observation b749b5c8-eb9d-40b6-817f-efa60714d0dd · outbound

This paper cites Williamson.

Corruptions of Supervised Learning Problems: Typology and Mitigations Williamson

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.014283Z

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Observation 5445b663-6c8e-4e46-954b-1c848bb12af0 · outbound

This paper cites Learning from binary labels with instance-dependent noise.Machine Learning, 107(8):1561–1595.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning from binary labels with instance-dependent noise.Machine Learning, 107(8):1561–1595

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.796959Z

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Observation 34a628d0-b4ab-4c45-bee5-2568cfdbfcc0 · outbound

This paper cites Learning with bounded instance and label-dependent label noise.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning with bounded instance and label-dependent label noise

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.760783Z

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Observation 2761cbf5-dc96-44a7-b519-7617f0a7277c · outbound

This paper cites Instance- dependent label-noise learning under a structural causal model.Advances in Neural Information Processing Systems, 34:4409–4420.

Corruptions of Supervised Learning Problems: Typology and Mitigations Instance- dependent label-noise learning under a structural causal model.Advances in Neural Information Processing Systems, 34:4409–4420

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.007190Z

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Observation f24579b3-08e8-4b0d-b1fc-c4f6f07aa0a3 · outbound

This paper cites Tackling instance-dependent label noise via a universal probabilistic model.

Corruptions of Supervised Learning Problems: Typology and Mitigations Tackling instance-dependent label noise via a universal probabilistic model

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.768094Z

Source-reported events for the cited work

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Observation e64071ec-419b-4464-a771-7805d22f3d3c · outbound

This paper cites Decontamination of mutually contaminated models.

Corruptions of Supervised Learning Problems: Typology and Mitigations Decontamination of mutually contaminated models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.880004Z

Source-reported events for the cited work

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Observation 1aeeea57-a15c-4993-af10-139fef82e380 · outbound

This paper cites Learning from corrupted binary labels via class-probability estimation.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning from corrupted binary labels via class-probability estimation

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.000152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e45be7f1-f315-4ac5-8d5a-176a48f20b74 · outbound

This paper cites Classi- fication with asymmetric label noise: Consistency and maximal denoising.Electronic Journal of Statistics, 10(2):2780–2824.

Corruptions of Supervised Learning Problems: Typology and Mitigations Classi- fication with asymmetric label noise: Consistency and maximal denoising.Electronic Journal of Statistics, 10(2):2780–2824

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.003756Z

Source-reported events for the cited work

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Observation c079640b-8f99-415b-b6e2-eb570e441014 · outbound

This paper cites Decontamination of mutual contamination models.Journal of machine learning research, 20(41).

Corruptions of Supervised Learning Problems: Typology and Mitigations Decontamination of mutual contamination models.Journal of machine learning research, 20(41)

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.996264Z

Source-reported events for the cited work

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Observation f5b1d02c-fbfd-4199-8e60-c7287daec767 · outbound

This paper cites The class imbalance problem: A systematic study.

Corruptions of Supervised Learning Problems: Typology and Mitigations The class imbalance problem: A systematic study

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.010808Z

Source-reported events for the cited work

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Observation 383eef82-f95a-4dd0-bf5a-27c7a2f9d866 · outbound

This paper cites Learning from imbalanced data.IEEE Transactions on knowledge and data engineering, 21(9):1263–1284.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning from imbalanced data.IEEE Transactions on knowledge and data engineering, 21(9):1263–1284

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.753742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fa374efd-265a-4961-ae30-7aa12a473dba · outbound

This paper cites A systematic study of the class imbalance problem in convolutional neural networks.Neural networks, 106:249–259.

Corruptions of Supervised Learning Problems: Typology and Mitigations A systematic study of the class imbalance problem in convolutional neural networks.Neural networks, 106:249–259

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.021203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4e1ae92c-a377-441f-ac9a-a6d36048a213 · outbound

This paper cites Detecting and correcting for label shift with black box predictors.

Corruptions of Supervised Learning Problems: Typology and Mitigations Detecting and correcting for label shift with black box predictors

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.028520Z

Source-reported events for the cited work

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Observation c6d08b08-28c5-4a18-ba88-71035eecfa0b · outbound

This paper cites Covariate shift by kernel mean matching.Dataset shift in machine learning, 3(4):5.

Corruptions of Supervised Learning Problems: Typology and Mitigations Covariate shift by kernel mean matching.Dataset shift in machine learning, 3(4):5

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.031980Z

Source-reported events for the cited work

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Observation 832adf0c-b22f-43ae-a6ba-650b19d013e5 · outbound

This paper cites MIT press.

Corruptions of Supervised Learning Problems: Typology and Mitigations MIT press

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.804041Z

Source-reported events for the cited work

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Observation bc2fb4d2-f8fd-4f2f-a567-3eea55792589 · outbound

This paper cites Domainadaptationwithconditionaltransferablecomponents.

Corruptions of Supervised Learning Problems: Typology and Mitigations Domainadaptationwithconditionaltransferablecomponents

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.891060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8bc79fe4-3d95-4fe2-a1e7-01329a0e8624 · outbound

This paper cites Label-noiserobustdomainadaptation.

Corruptions of Supervised Learning Problems: Typology and Mitigations Label-noiserobustdomainadaptation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.813522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 853e65b4-bc9c-4bed-8aed-b868bbc1a8a7 · outbound

This paper cites A Neural Algorithm of Artistic Style.

Corruptions of Supervised Learning Problems: Typology and Mitigations A Neural Algorithm of Artistic Style

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Resolution: verified exact; local_arxiv, observed 2026-05-24T07:16:03.141617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b91f7ce5-58f3-45df-bec3-943341301700 · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution.

Corruptions of Supervised Learning Problems: Typology and Mitigations Perceptual losses for real-time style transfer and super-resolution

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.938722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 76b12423-258f-457a-abd0-ebbc172fe062 · outbound

This paper cites Audio style transfer.

Corruptions of Supervised Learning Problems: Typology and Mitigations Audio style transfer

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.825896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 11d9df13-2c6a-4b99-9a6b-38840600dd38 · outbound

This paper cites Intriguing properties of neural networks.

Corruptions of Supervised Learning Problems: Typology and Mitigations Intriguing properties of neural networks

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-05-24T07:16:03.106186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9f951211-b6ee-4f46-9a4f-97c91d68d56d · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Corruptions of Supervised Learning Problems: Typology and Mitigations Explaining and Harnessing Adversarial Examples

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-05-24T07:16:03.131517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 60328df0-1faa-478e-a694-414c36841ce7 · outbound

This paper cites Thelimitationsofdeeplearninginadversarialsettings.

Corruptions of Supervised Learning Problems: Typology and Mitigations Thelimitationsofdeeplearninginadversarialsettings

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.942009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d322613b-6ea7-49c5-b687-a7ef4147cf3f · outbound

This paper cites Adversarialexamplesinthephysical world.

Corruptions of Supervised Learning Problems: Typology and Mitigations Adversarialexamplesinthephysical world

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.106630Z

Source-reported events for the cited work

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Observation 49334474-3b0d-42bd-b8ad-974d884a9f93 · outbound

This paper cites Natural adversarial examples.

Corruptions of Supervised Learning Problems: Typology and Mitigations Natural adversarial examples

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.965597Z

Source-reported events for the cited work

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Observation a9a0abe1-7b3b-43d5-b4f7-60e36aac98a1 · outbound

This paper cites Learning in the presence of concept drift and hidden contexts.Machine learning, 23:69–101.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning in the presence of concept drift and hidden contexts.Machine learning, 23:69–101

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.945805Z

Source-reported events for the cited work

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Observation fc31c1be-5876-47c1-8eea-886535fe4652 · outbound

This paper cites A survey on concept drift adaptation.ACM computingsurveys (CSUR), 46(4): 1–37.

Corruptions of Supervised Learning Problems: Typology and Mitigations A survey on concept drift adaptation.ACM computingsurveys (CSUR), 46(4): 1–37

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.867330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c5854268-7cd9-4f26-8927-6e26609743cb · outbound

This paper cites Learning underconceptdrift: Areview.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning underconceptdrift: Areview

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.870403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ac72b490-afa2-4db5-8112-c532fc28e875 · outbound

This paper cites Entropy-based concept shift detection.

Corruptions of Supervised Learning Problems: Typology and Mitigations Entropy-based concept shift detection

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.935433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5ef68685-1622-4261-a7af-e2b7d68d3a6e · outbound

This paper cites Effective learning in dynamic environments by explicit context tracking.

Corruptions of Supervised Learning Problems: Typology and Mitigations Effective learning in dynamic environments by explicit context tracking

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.969280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c99da369-b45e-4d1b-bad4-0b5d9b3c84e3 · outbound

This paper cites Tolerating concept and sampling shift in lazy learning using prediction error context switching.Artificial Intelligence Review, 11:133–155.

Corruptions of Supervised Learning Problems: Typology and Mitigations Tolerating concept and sampling shift in lazy learning using prediction error context switching.Artificial Intelligence Review, 11:133–155

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.928981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bddb82b4-565a-4a2f-af6d-7fe1e5a5102a · outbound

This paper cites The problem of concept drift: definitions and related work.Computer Science Department, Trinity College Dublin, 106(2):58.

Corruptions of Supervised Learning Problems: Typology and Mitigations The problem of concept drift: definitions and related work.Computer Science Department, Trinity College Dublin, 106(2):58

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.952101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a769060e-35e3-4098-9c70-aa63151ecb75 · outbound

This paper cites Springer.

Corruptions of Supervised Learning Problems: Typology and Mitigations Springer

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.948767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation da9a4ab7-0bb8-4bfc-b676-d5fd7e3f8275 · outbound

This paper cites Springer.

Corruptions of Supervised Learning Problems: Typology and Mitigations Springer

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.858972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5435ae24-b705-498b-bd2e-2b26f1aff6ab · outbound

This paper cites Springer.

Corruptions of Supervised Learning Problems: Typology and Mitigations Springer

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.873884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bb28f7dd-2102-4378-baa4-f556bbc2e89c · outbound

This paper cites StatisticalCausalModellingandDecisionTheory .PhDthesis,TheAustralian National University.

Corruptions of Supervised Learning Problems: Typology and Mitigations StatisticalCausalModellingandDecisionTheory .PhDthesis,TheAustralian National University

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.063360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0d57543a-b2c0-48ab-ba48-250f40be944f · outbound

This paper cites Kleisli categories and probability - 03 - markov kernels.https: //youtu.be/psUDrasc21o?si=we87QEeKiGOa0_eN.

Corruptions of Supervised Learning Problems: Typology and Mitigations Kleisli categories and probability - 03 - markov kernels.https: //youtu.be/psUDrasc21o?si=we87QEeKiGOa0_eN

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.095734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 93d256e2-8c4c-4f0a-b0fa-9faf9e278069 · outbound

This paper cites A class of measures of informativity of observation channels.Periodica Mathematica Hungarica, 2(1-4):191–213.

Corruptions of Supervised Learning Problems: Typology and Mitigations A class of measures of informativity of observation channels.Periodica Mathematica Hungarica, 2(1-4):191–213

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.898905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation edcc3d1e-c353-49c8-9e0e-b8eb8d3129f6 · outbound

This paper cites Cambridge University Press.

Corruptions of Supervised Learning Problems: Typology and Mitigations Cambridge University Press

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.807192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4a1ef189-5905-4883-ab02-0186f150603d · outbound

This paper cites World Scientific.

Corruptions of Supervised Learning Problems: Typology and Mitigations World Scientific

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.887783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aaf475cb-eac6-4838-9764-87875e758349 · outbound

This paper cites Everyone wants to do the model work, not the data work.

Corruptions of Supervised Learning Problems: Typology and Mitigations Everyone wants to do the model work, not the data work

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.922333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cedd25af-63c0-4299-a1ad-577f71a03410 · outbound

This paper cites Convexity, classification, and risk bounds.Journal of the American Statistical Association, 101(473):138–156.

Corruptions of Supervised Learning Problems: Typology and Mitigations Convexity, classification, and risk bounds.Journal of the American Statistical Association, 101(473):138–156

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.073564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 52ad4e3f-b48d-4642-b7db-28ac40f9a795 · outbound

This paper cites A theory of learning from different domains.Machine Learning, 79: 151–175.

Corruptions of Supervised Learning Problems: Typology and Mitigations A theory of learning from different domains.Machine Learning, 79: 151–175

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.078151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 201392e8-f164-45a4-9c26-9090b668db77 · outbound

This paper cites Fairness evaluation in presence of biased noisy labels.

Corruptions of Supervised Learning Problems: Typology and Mitigations Fairness evaluation in presence of biased noisy labels

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.087978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4ee04386-9275-476e-9171-16849303036b · outbound

This paper cites How the war on drugs damages black social mobility.The Brookings Institution, published Sept, 30.

Corruptions of Supervised Learning Problems: Typology and Mitigations How the war on drugs damages black social mobility.The Brookings Institution, published Sept, 30

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.883260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6e980418-dab0-4271-a0aa-f7f559dd35c1 · outbound

This paper cites Learningclassifiersfromonlypositiveandunlabeleddata.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learningclassifiersfromonlypositiveandunlabeleddata

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.103712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation faaf9b7a-ca62-4457-a4f7-1680913d1d2a · outbound

This paper cites Presence-only data and the EM algorithm.Biometrics, 65(2):554–563.

Corruptions of Supervised Learning Problems: Typology and Mitigations Presence-only data and the EM algorithm.Biometrics, 65(2):554–563

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.800471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 13e7504b-08c5-4cca-9022-a6c34eb1b57d · outbound

This paper cites Analysis of learning from positive and unlabeled data.Advances in neural information processing systems, 27.

Corruptions of Supervised Learning Problems: Typology and Mitigations Analysis of learning from positive and unlabeled data.Advances in neural information processing systems, 27

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.052932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4ca2e76b-23b3-47a5-addb-cc2d4404cc58 · outbound

This paper cites Convex formulation for learning from positive and unlabeled data.

Corruptions of Supervised Learning Problems: Typology and Mitigations Convex formulation for learning from positive and unlabeled data

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.049298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dcc9b5c1-2e0e-4dc1-a985-bbcfe10c9cfe · outbound

This paper cites Positive- unlabeled learning with non-negative risk estimator.Advances in neural information processing systems, 30.

Corruptions of Supervised Learning Problems: Typology and Mitigations Positive- unlabeled learning with non-negative risk estimator.Advances in neural information processing systems, 30

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.056365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ef4594c8-a8d1-4b77-bf30-cbd92bb49ff2 · outbound

This paper cites Estimating labels from label proportions.

Corruptions of Supervised Learning Problems: Typology and Mitigations Estimating labels from label proportions

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.045799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 00259bd5-64b3-4b2d-8158-b0bdd7a29506 · outbound

This paper cites On Learning from Label Proportions.

Corruptions of Supervised Learning Problems: Typology and Mitigations On Learning from Label Proportions

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-05-24T07:16:03.136688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6d2c4b69-bb1d-45a3-b6ee-6c5eafa7388a · outbound

This paper cites Learning from label proportionswithgenerativeadversarialnetworks.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning from label proportionswithgenerativeadversarialnetworks

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.059922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 40bfe590-ea9e-4bc6-b449-1ba9b92bd54f · outbound

This paper cites Learning from label proportions: A mutual contam- ination framework.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning from label proportions: A mutual contam- ination framework

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.024721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 12b99de7-d7b5-440b-89d6-33560a3ad8ab · outbound

This paper cites Multi-class classification from multiple unlabeled datasets with partial risk regularization.

Corruptions of Supervised Learning Problems: Typology and Mitigations Multi-class classification from multiple unlabeled datasets with partial risk regularization

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.035281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b5fbab3c-5143-42b0-86db-b654c279370e · outbound

This paper cites Comparisonofexperiments.

Corruptions of Supervised Learning Problems: Typology and Mitigations Comparisonofexperiments

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.038611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 45fc0d1d-69fd-4818-bb54-d382d85523c0 · outbound

This paper cites Machinelearningviatransitions.

Corruptions of Supervised Learning Problems: Typology and Mitigations Machinelearningviatransitions

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.070080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9934eda5-6353-4844-85b6-135fa579cb4a · outbound

This paper cites Data corruption — Wikipedia, the free encyclopedia.https: //en.wikipedia.org/w/index.php?title=Data_corruption&oldid=1176791517.

Corruptions of Supervised Learning Problems: Typology and Mitigations Data corruption — Wikipedia, the free encyclopedia.https: //en.wikipedia.org/w/index.php?title=Data_corruption&oldid=1176791517

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.117742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b011e5f-d5c1-48b1-a9b6-a9cde902cb4e · outbound

This paper cites Loss functions for binary class probability estimation and classification: Structure and applications.Working draft, November.

Corruptions of Supervised Learning Problems: Typology and Mitigations Loss functions for binary class probability estimation and classification: Structure and applications.Working draft, November

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.829605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4d26acd5-811f-40a9-a9ac-cf4b00a848bd · outbound

This paper cites Composite multiclass losses.Journal of machine learning research, 17(222):1–52.

Corruptions of Supervised Learning Problems: Typology and Mitigations Composite multiclass losses.Journal of machine learning research, 17(222):1–52

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.955104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e93f30df-99a5-4dd4-88d6-65c92d7c35ae · outbound

This paper cites Learning bounds for importance weighting.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning bounds for importance weighting

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.925519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3816e662-f814-4f52-8c9a-7b93ae19643e · outbound

This paper cites Classificationwithnoisylabelsbyimportancereweight- ing.

Corruptions of Supervised Learning Problems: Typology and Mitigations Classificationwithnoisylabelsbyimportancereweight- ing

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.932109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fb3847d2-da5a-4b17-8ee7-c17939eee69d · outbound

This paper cites Rethinking im- portance weighting for deep learning under distribution shift.

Corruptions of Supervised Learning Problems: Typology and Mitigations Rethinking im- portance weighting for deep learning under distribution shift

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.958391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2531a390-129d-4cd1-9136-69f19b1d35b8 · outbound

This paper cites an unresolved cited work.

Corruptions of Supervised Learning Problems: Typology and Mitigations Unresolved cited work

Reference 89

Resolution: unresolved; raw_fallback, observed 2026-05-24T07:16:03.855687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ccc56802-5483-478a-a88f-ee8df965e29e · outbound

This paper cites Generalizing importance weightingtoauniversalsolverfordistributionshiftproblems.

Corruptions of Supervised Learning Problems: Typology and Mitigations Generalizing importance weightingtoauniversalsolverfordistributionshiftproblems

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.862260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation be9d09ff-7185-401f-af6b-0d2e734a30fb · outbound

This paper cites A rate of convergence for mixture proportion estimation, with application to learning from noisy labels.

Corruptions of Supervised Learning Problems: Typology and Mitigations A rate of convergence for mixture proportion estimation, with application to learning from noisy labels

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.876987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 83808b7e-b6f7-4bc3-9bb5-81dedd3f08f4 · outbound

This paper cites statisticalinferencewithnon-probabilitysurveysamples.

Corruptions of Supervised Learning Problems: Typology and Mitigations statisticalinferencewithnon-probabilitysurveysamples

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.894355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1e7d308e-c6dd-4e25-b70f-9881d0e3fffb · outbound

This paper cites Weneedtotalkaboutnonprobability samples.

Corruptions of Supervised Learning Problems: Typology and Mitigations Weneedtotalkaboutnonprobability samples

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.041837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 845a2f20-a8fe-4888-b8fa-95f3aca964df · outbound

This paper cites Inference and missing data.Biometrika, 63(3):581–592.

Corruptions of Supervised Learning Problems: Typology and Mitigations Inference and missing data.Biometrika, 63(3):581–592

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:04.067008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c10602df-b841-4efd-8a04-edace5bc9c50 · outbound

This paper cites John Wiley & Sons.

Corruptions of Supervised Learning Problems: Typology and Mitigations John Wiley & Sons

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.842969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 02810281-8a37-45a4-87ec-9fdbe0047a11 · outbound

This paper cites Learning from comple- mentary labels.Advances in neural information processing systems, 30.

Corruptions of Supervised Learning Problems: Typology and Mitigations Learning from comple- mentary labels.Advances in neural information processing systems, 30

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.810374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2a31cfaa-0ab6-4acd-ae84-3c60cb51141a · outbound

This paper cites Complementary-label learning for arbitrary losses and models.

Corruptions of Supervised Learning Problems: Typology and Mitigations Complementary-label learning for arbitrary losses and models

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.822915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b7d7665d-bb87-43d2-b2a5-407c4c6a54d8 · outbound

This paper cites Making risk minimization tolerant to label noise.Neurocomputing, 160:93–107.

Corruptions of Supervised Learning Problems: Typology and Mitigations Making risk minimization tolerant to label noise.Neurocomputing, 160:93–107

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.972485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ea717795-db9a-40cc-99a5-33ee66602c38 · outbound

This paper cites Modelling class noise with symmetric and asymmetric distribu- tions.

Corruptions of Supervised Learning Problems: Typology and Mitigations Modelling class noise with symmetric and asymmetric distribu- tions

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.846309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b0c1a8ec-6177-4d7d-806e-a5f91cf561a2 · outbound

This paper cites Domain adaptation as a problem of inference on graphical models.Advances in neural information processing systems, 33:4965–4976.

Corruptions of Supervised Learning Problems: Typology and Mitigations Domain adaptation as a problem of inference on graphical models.Advances in neural information processing systems, 33:4965–4976

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T07:16:03.849474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers