Learning from Noisy Labels via Conditional Distributionally Robust Optimization | Paper Citation Record

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This paper cites Deep Learning.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Deep Learning

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This paper cites Deep learning in neural networks: An overview.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Deep learning in neural networks: An overview

Reference 2

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This paper cites A closer look at memorization in deep networks.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization A closer look at memorization in deep networks

Reference 3

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This paper cites Measurement Error in Nonlinear Models: A Modern Perspective.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Measurement Error in Nonlinear Models: A Modern Perspective

Reference 4

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This paper cites Statistical Analysis with Measurement Error or Misclassification.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Statistical Analysis with Measurement Error or Misclassification

Reference 5

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This paper cites Handbook of Measurement Error Models.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Handbook of Measurement Error Models

Reference 6

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This paper cites Maximum likelihood estimation of observer error-rates using the em algorithm.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Maximum likelihood estimation of observer error-rates using the em algorithm

Reference 7

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Observation dfe9ed69-e26c-4017-8701-adb88f46a3e0 · outbound

This paper cites Whose vote should count more: Optimal integration of labels from labelers of unknown expertise.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Whose vote should count more: Optimal integration of labels from labelers of unknown expertise

Reference 8

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Observation 1d3bbc64-9819-4b39-92ea-3c85d8737f0e · outbound

This paper cites Learning From Noisy Singly-labeled Data.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Learning From Noisy Singly-labeled Data

Reference 9

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This paper cites Label correction of crowdsourced noisy annotations with an instance-dependent noise transition model.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Label correction of crowdsourced noisy annotations with an instance-dependent noise transition model

Reference 10

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Observation a561e655-1412-4e5c-9b77-81814a15d976 · outbound

This paper cites Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds

Reference 11

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Observation ee921e7b-39c6-4a57-ac63-b8325d54b62f · outbound

This paper cites Learning from noisy labels by regularized estimation of annotator confusion.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Learning from noisy labels by regularized estimation of annotator confusion

Reference 12

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This paper cites Pre-train your loss: Easy bayesian transfer learning with informative priors.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Pre-train your loss: Easy bayesian transfer learning with informative priors

Reference 13

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Observation 3addde19-3687-424c-8bc0-bfbe925e8df2 · outbound

This paper cites Are anchor points really indispensable in label-noise learning? In Advances in Neural Information Processing Systems, volume 32, pages 6838–6849, 2019.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Are anchor points really indispensable in label-noise learning? In Advances in Neural Information Processing Systems, volume 32, pages 6838–6849, 2019

Reference 14

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This paper cites Adversarial interpretation of bayesian inference.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Adversarial interpretation of bayesian inference

Reference 15

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Observation 77dcc4a0-c8f5-45d0-bb40-73c0cc10d674 · outbound

This paper cites Conditional distributionally robust functionals.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Conditional distributionally robust functionals

Reference 16

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Observation e7f49ee3-e985-4dc6-aa85-ac599dd8c110 · outbound

This paper cites Quantifying distributional model risk via optimal transport.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Quantifying distributional model risk via optimal transport

Reference 17

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This paper cites Distributionally robust stochastic optimization with Wasserstein distance.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Distributionally robust stochastic optimization with Wasserstein distance

Reference 18

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This paper cites Joint optimization framework for learning with noisy labels.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Joint optimization framework for learning with noisy labels

Reference 19

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This paper cites Cover and Joy A.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Cover and Joy A

Reference 20

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Observation 38f7c8f0-bd21-4ff5-80d7-d9b0e18f0793 · outbound

This paper cites Learning multiple layers of features from tiny images.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Learning multiple layers of features from tiny images

Reference 21

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This paper cites Learning with noisy labels revisited: A study using real-world human annotations.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Learning with noisy labels revisited: A study using real-world human annotations

Reference 22

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This paper cites Learning supervised topic models for classification and regression from crowds.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Learning supervised topic models for classification and regression from crowds

Reference 23

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Observation a46c7d7d-48f8-426f-ac49-7513771f1b4c · outbound

This paper cites Russell, and Jenny Yuen.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Russell, and Jenny Yuen

Reference 24

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This paper cites SELFIE: Refurbishing unclean samples for robust deep learning.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization SELFIE: Refurbishing unclean samples for robust deep learning

Reference 25

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This paper cites Deep residual learning for image recognition.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Deep residual learning for image recognition

Reference 26

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This paper cites Deep learning from crowds.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Deep learning from crowds

Reference 27

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This paper cites Very deep convolutional networks for large-scale image recogni- tion.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Very deep convolutional networks for large-scale image recogni- tion

Reference 28

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This paper cites Part-dependent label noise: Towards instance-dependent label noise.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Part-dependent label noise: Towards instance-dependent label noise

Reference 29

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This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Co-teaching: Robust training of deep neural networks with extremely noisy labels

Reference 30

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This paper cites How does disagreement help generalization against label corruption? In Proceedings of the 36th International Conference on Machine Learning, volume 97, pages 7164–7173, 2019.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization How does disagreement help generalization against label corruption? In Proceedings of the 36th International Conference on Machine Learning, volume 97, pages 7164–7173, 2019

Reference 31

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This paper cites Combating noisy labels with sample selection by mining high-discrepancy examples.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Combating noisy labels with sample selection by mining high-discrepancy examples

Reference 32

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This paper cites Mitigating memorization of noisy labels by clipping the model prediction.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Mitigating memorization of noisy labels by clipping the model prediction

Reference 33

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This paper cites Who said what: Modeling individual labelers improves classification.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Who said what: Modeling individual labelers improves classification

Reference 34

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This paper cites Learning from crowds by modeling common confusions.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Learning from crowds by modeling common confusions

Reference 35

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This paper cites Coupled confusion correction: Learning from crowds with sparse annotations.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Coupled confusion correction: Learning from crowds with sparse annotations

Reference 36

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This paper cites Linear and Nonlinear Programming, volume 2.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Linear and Nonlinear Programming, volume 2

Reference 37

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This paper cites Applied Mathematical Programming.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Applied Mathematical Programming

Reference 38

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This paper cites High-Dimensional Statistics: A Non-Asymptotic Viewpoint.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization High-Dimensional Statistics: A Non-Asymptotic Viewpoint

Reference 39

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This paper cites Minimax statistical learning with wasserstein distances.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Minimax statistical learning with wasserstein distances

Reference 40

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This paper cites The supremum and infimum.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization The supremum and infimum

Reference 41

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This paper cites Foundations of Machine Learning.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Foundations of Machine Learning

Reference 42

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This paper cites Adam: A Method for Stochastic Optimization.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Adam: A Method for Stochastic Optimization

Reference 43

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This paper cites Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing

Reference 44

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This paper cites Leverag- ing inter-rater agreement for classification in the presence of noisy labels.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Leverag- ing inter-rater agreement for classification in the presence of noisy labels

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.956696Z

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This paper cites Error-bounded correction of noisy labels.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Error-bounded correction of noisy labels

Reference 46

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This paper cites parameter.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization parameter

Reference 47

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This paper cites We complete the proof by considering the following two cases.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization We complete the proof by considering the following two cases

Reference 48

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Learning from Noisy Labels via Conditional Distributionally Robust Optimization Unresolved cited work

Reference 49

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Learning from Noisy Labels via Conditional Distributionally Robust Optimization Unresolved cited work

Reference 50

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This paper cites Step 2.2: Assume T is convex.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Step 2.2: Assume T is convex

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.861220Z

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Observation a2203b8a-eefd-4f6b-baff-175b46dac46a · outbound

This paper cites Thus, ψ∗ 1 = ψ⋄ 1 with γ∗ ψ∗ 1 = T (ψ⋄ 1 )−T (1−ψ⋄ 1 ) κp by (A22).

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Thus, ψ∗ 1 = ψ⋄ 1 with γ∗ ψ∗ 1 = T (ψ⋄ 1 )−T (1−ψ⋄ 1 ) κp by (A22)

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.845605Z

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Observation 818e238e-d4b2-4d79-9433-23c6f5b247b9 · outbound

This paper cites 25 • Step 2: If P1 ≥ ϱ(ϵ), then, by (A22), γ∗ ψ∗ 2 is set as γ∗ ψ ∗ 2 = T (ψ ∗ 2 )−T (1−ψ ∗ 2 ) κp = T (1−ψ∗ 2 )−T (ψ∗ 2 ) κp.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization 25 • Step 2: If P1 ≥ ϱ(ϵ), then, by (A22), γ∗ ψ∗ 2 is set as γ∗ ψ ∗ 2 = T (ψ ∗ 2 )−T (1−ψ ∗ 2 ) κp = T (1−ψ∗ 2 )−T (ψ∗ 2 ) κp

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.829870Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0095a947-f9bc-4f95-b5b2-b7c1eb735627 · outbound

This paper cites – If P1 > ϱ(ϵ) + T (0)−T (1/2) T (0)−T (1) , then ψ ∗ 2 = 0 and γ∗ ψ∗ 2 = T (0)−T (1) κp.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization – If P1 > ϱ(ϵ) + T (0)−T (1/2) T (0)−T (1) , then ψ ∗ 2 = 0 and γ∗ ψ∗ 2 = T (0)−T (1) κp

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.814170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 556a500a-192b-46ec-b034-1fa68b4d6c55 · outbound

This paper cites • Step 2.2: Assume T is convex.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization • Step 2.2: Assume T is convex

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.798697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f8a17f71-4cad-47dd-b063-f132c86142c8 · outbound

This paper cites – If ϱ(ϵ) ≤ P1 ≤ ϱ(ϵ) + 1 2, then ψ ∗ 2 = 1 2 and γ∗ ψ∗ 2 = 0.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization – If ϱ(ϵ) ≤ P1 ≤ ϱ(ϵ) + 1 2, then ψ ∗ 2 = 1 2 and γ∗ ψ∗ 2 = 0

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.783141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2c558177-1974-40f2-84eb-73e093ea2ef2 · outbound

This paper cites an unresolved cited work.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-12T12:36:43.767285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 58ce3f7e-5b20-40dc-ba29-f81b95d8172f · outbound

This paper cites an unresolved cited work.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Unresolved cited work

Reference 58

Resolution: parse uncertain; raw_fallback, observed 2026-08-12T12:36:43.751901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 49e278e7-34ed-4a34-a8c1-e51063bf9dbc · outbound

This paper cites In summary, we present the derived results in Tables 3 and 4 for the scenarios whereT is concave and convex, respectively.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization In summary, we present the derived results in Tables 3 and 4 for the scenarios whereT is concave and convex, respectively

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.736020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 71d38792-4056-4784-bdfb-156fceec2cc0 · outbound

This paper cites Update by Disagreement.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Update by Disagreement

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.719890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c38e3542-0272-4512-9d85-107c40321234 · outbound

This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.703975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2823558c-eaa8-48ba-89d4-8b7314793a91 · outbound

This paper cites Limitations.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Limitations

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.688117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 67558c91-f53e-4feb-b83f-caff7bce54ee · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.671723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4b051042-7e1e-436d-b6f0-d573b426b24e · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that the paper does not include experiments

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.653827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 164beeeb-174e-4896-967b-91529d9a36e6 · outbound

This paper cites Guidelines: • The answer NA means that paper does not include experiments requiring code.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.637268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 37af4b35-7423-48a3-9e1d-991be8c2266d · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that the paper does not include experiments

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.620856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 50b72063-401a-4717-a449-c597d5f38635 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that the paper does not include experiments

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.604790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f82622fb-9724-43e7-b4c5-a50049afad74 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that the paper does not include experiments

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.588490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 93aef465-7657-44be-9a32-6e1d37842b0a · outbound

This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T12:36:43.342093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dee5b9a0-64cb-40d1-adcb-2c86eaf6416b · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.561231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e115b4c4-67ad-4e84-90af-38176ca5191f · outbound

This paper cites Guidelines: • The answer NA means that the paper poses no such risks.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that the paper poses no such risks

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.543894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d73f59fe-1881-4525-993f-548a93b51447 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that the paper does not use existing assets

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.527924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 31aa51b9-f555-4bfc-b1b4-91f5381ddea9 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not release new assets.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that the paper does not release new assets

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.511640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4cdba0a7-6124-40a8-b4bb-43fc2d7cb83a · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.495794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2f76ce4d-fbd8-4317-ab34-fc51b59a43a9 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Learning from Noisy Labels via Conditional Distributionally Robust Optimization Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:43.479750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers