Data Cleaning papers, 2023–2026

Key findings

• Data Cleaning fell from 21 papers in 2023 to 10 in 2026, so interest is cooling.
• ICML publishes the most Data Cleaning work: 17 papers from 2023 to 2026, 35% of the four-venue total.
• Among the 5 topics under Data-Centric AI, it ranks #5 by 2026 paper count; the fastest grower in the group, Synthetic Data Generation, is up 1.3x versus 0.5x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026GradTrack: Detecting Noisy Labels via Temporal Trajectories of Class-wise Gradient Misalignment
• ICML 2026Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
• ICLR 2026Enhancing Learning with Noisy Labels via Rockafellian Relaxation
• CVPR 2026Cleaning the Pool Progressive Filtering of Unlabeled Pools in Deep
• NeurIPS 2025Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples
• ICML 2025Free Process Rewards without Process Labels
• ICLR 2025Learning to Generate Diverse Pedestrian Movements from Web Videos with Noisy Labels
• CVPR 2025Directional Label Diffusion Model for Learning from Noisy Labels
• ICML 2024Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical
• CVPR 2024Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning
• NeurIPS 2023Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model
• ICML 2023Data Structures for Density Estimation
• ICLR 2023Learning to Segment from Noisy Annotations: A Spatial Correction Approach
• CVPR 2023Fine-Grained Classification With Noisy Labels
• NeurIPS 2026Memorization Is Folding: Topological Signatures of Noisy-Label Learning
• CVPR 2026Debiased Sample Selection for Learning with Noisy Labels
• NeurIPS 2025ELDET: Early-Learning Distillation with Noisy Labels for Object Detection
• ICML 2025Retraining with Predicted Hard Labels Provably Increases Model Accuracy
• ICML 2024Pi-DUAL: Using privileged information to distinguish clean from noisy labels
• CVPR 2024Learning with Structural Labels for Learning with Noisy Labels

All Data Cleaning papers

48 papers, page 1 of 1

1. CVPR 2026Cleaning the Pool Progressive Filtering of Unlabeled Pools in Deep
2. CVPR 2026Debiased Sample Selection for Learning with Noisy Labels
3. ICLR 2026Enhancing Learning with Noisy Labels via Rockafellian Relaxation
4. ICML 2026Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
5. NeurIPS 2026GradTrack: Detecting Noisy Labels via Temporal Trajectories of Class-wise Gradient Misalignment
6. NeurIPS 2026Hidden Tails: Certifying Tail-Risk Claims under Selective Labels
7. NeurIPS 2026Memorization Is Folding: Topological Signatures of Noisy-Label Learning
8. NeurIPS 2026Noisy Data is Destructive to Reinforcement Learning with Verifiable Rewards
9. NeurIPS 2026Robust Noisy Inductive Matrix Completion with Local Linear Convergence
10. NeurIPS 2026Self-Cleaning Diffusion Models
11. CVPR 2025Directional Label Diffusion Model for Learning from Noisy Labels
12. ICLR 2025Learning to Generate Diverse Pedestrian Movements from Web Videos with Noisy Labels
13. ICML 2025Free Process Rewards without Process Labels
14. ICML 2025Retraining with Predicted Hard Labels Provably Increases Model Accuracy
15. NeurIPS 2025ELDET: Early-Learning Distillation with Noisy Labels for Object Detection
16. NeurIPS 2025Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples
17. CVPR 2024A Noisy Elephant in the Room: Is Your Out-of-Distribution Detector Robust to Label Noise?
18. CVPR 2024Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning
19. CVPR 2024JoAPR: Cleaning the Lens of Prompt Learning for Vision-Language Models
20. CVPR 2024Learning with Structural Labels for Learning with Noisy Labels
21. CVPR 2024Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning
22. CVPR 2024Towards Generalizing to Unseen Domains with Few Labels
23. ICML 2024Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data
24. ICML 2024Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical
25. ICML 2024Pi-DUAL: Using privileged information to distinguish clean from noisy labels
26. ICML 2024Provably Robust DPO: Aligning Language Models with Noisy Feedback
27. ICML 2024Self-cognitive Denoising in the Presence of Multiple Noisy Label Sources
28. CVPR 2023Fine-Grained Classification With Noisy Labels
29. CVPR 2023HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization
30. CVPR 2023Learning Articulated Shape With Keypoint Pseudo-Labels From Web Images
31. CVPR 2023Learning From Noisy Labels With Decoupled Meta Label Purifier
32. CVPR 2023Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels
33. CVPR 2023Noisy Correspondence Learning With Meta Similarity Correction
34. CVPR 2023OT-Filter: An Optimal Transport Filter for Learning With Noisy Labels
35. CVPR 2023Twin Contrastive Learning With Noisy Labels
36. ICLR 2023Learning to Segment from Noisy Annotations: A Spatial Correction Approach
37. ICLR 2023Mitigating Memorization of Noisy Labels via Regularization between Representations
38. ICML 20232D-Shapley: A Framework for Fragmented Data Valuation
39. ICML 2023Data Structures for Density Estimation
40. ICML 2023Data-Derived Weak Universal Consistency
41. ICML 2023Generating Private Synthetic Data with Genetic Algorithms
42. ICML 2023Gradient-Free Structured Pruning with Unlabeled Data
43. ICML 2023Learning Functional Distributions with Private Labels
44. ICML 2023Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation
45. ICML 2023Mitigating Memorization of Noisy Labels by Clipping the Model Prediction
46. ICML 2023Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data
47. NeurIPS 2023Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model
48. NeurIPS 2023Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy

FAQ

How many Data Cleaning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

48 in total: 21 in 2023, 11 in 2024, 6 in 2025, 10 in 2026.

How many Data Cleaning papers are at NeurIPS 2026?

NeurIPS 2026 has 6 Data Cleaning papers, versus 2 the year before.

How many Data Cleaning papers are at ICLR 2026?

ICLR 2026 has 1 Data Cleaning papers, versus 1 the year before.

How many Data Cleaning papers are at ICML 2026?

ICML 2026 has 1 Data Cleaning papers, versus 2 the year before.

How many Data Cleaning papers are at CVPR 2026?

CVPR 2026 has 2 Data Cleaning papers, versus 1 the year before.

Which conference publishes the most Data Cleaning papers?

ICML, with 17 papers from 2023 to 2026.