% Key papers for Loss correction and noise robustness theory (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2017_making_12448,
  title = {{Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{neurips2016_data_55661,
  title = {{Data Programming: Creating Large Training Sets, Quickly}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7031},
}

@inproceedings{aaai2018_reinforcement_106021,
  title = {{Reinforcement Learning for Relation Classification From Noisy Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/12063},
}

@inproceedings{cvpr2020_transfer_16379,
  title = {{Transfer Learning From Synthetic to Real-Noise Denoising With Adaptive Instance Normalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2018_modern_57390,
  title = {{Modern Neural Networks Generalize on Small Data Sets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11362},
}

@inproceedings{neurips2018_collaborative_56946,
  title = {{Collaborative Learning for Deep Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11196},
}

@inproceedings{neurips2020_self_60792,
  title = {{Self-Adaptive Training: beyond Empirical Risk Minimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17082},
}

@inproceedings{icml2016_training_36017,
  title = {{Training Deep Neural Networks via Direct Loss Minimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/songb16.html},
}

@inproceedings{cvpr2019_label_14372,
  title = {{Label-Noise Robust Generative Adversarial Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{aaai2016_re_105143,
  title = {{Re-Active Learning: Active Learning with Relabeling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10315},
}

@inproceedings{aaai2016_risk_105121,
  title = {{Risk Minimization in the Presence of Label Noise}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10293},
}

@inproceedings{iclr2021_unlearnable_30550,
  title = {{Unlearnable Examples: Making Personal Data Unexploitable}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2831},
}

@inproceedings{neurips2020_identifying_60041,
  title = {{Identifying Mislabeled Data using the Area Under the Margin Ranking}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18032},
}

@inproceedings{iclr2021_heteroskedastic_30611,
  title = {{Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2712},
}

@inproceedings{icml2022_robust_41120,
  title = {{Robust Training under Label Noise by Over-parameterization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17127},
}

@inproceedings{iclr2023_free_32316,
  title = {{Free Lunch for Domain Adversarial Training: Environment Label Smoothing}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11317},
}

@inproceedings{iclr2023_when_33313,
  title = {{When Source-Free Domain Adaptation Meets Learning with Noisy Labels}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11416},
}

@inproceedings{aaai2021_empower_513,
  title = {{Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/12675-empower-distantly-supervised-relation-extraction-with-collaborative-adversarial-training/},
}

@inproceedings{cvpr2021_autodo_16617,
  title = {{AutoDO: Robust AutoAugment for Biased Data With Label Noise via Scalable Probabilistic Implicit Differentiation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{icml2026_data_52249,
  title = {{Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=U1yt3NN0mC},
}

@inproceedings{aaai2024_dual_5759,
  title = {{Dual-Level Curriculum Meta-Learning for Noisy Few-Shot Learning Tasks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29392},
}

@inproceedings{aaai2024_ghost_6139,
  title = {{Ghost Noise for Regularizing Deep Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29228},
}

@inproceedings{cvpr2025_the_27859,
  title = {{The Impact Label Noise and Choice of Threshold has on Cross-Entropy and Soft-Dice in Image Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34621},
}

@inproceedings{icml2025_improved_48752,
  title = {{Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43870},
}

@inproceedings{neurips2024_stochastic_72770,
  title = {{Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94625},
}

@inproceedings{neurips2025_flowrefiner_76168,
  title = {{FlowRefiner: A Robust Traffic Classification Framework against Label Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=GKX9w731gu},
}

@inproceedings{iclr2024_early_33898,
  title = {{Early Stopping Against Label Noise Without Validation Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19177},
}

@inproceedings{aaai2025_decorrnet_8644,
  title = {{DeCorrNet: Enhancing Neural Decoding Performance by Eliminating Correlations in Noise}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33577},
}

@inproceedings{iclr2024_understanding_34101,
  title = {{Understanding Domain Generalization: A Noise Robustness Perspective}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18974},
}

@inproceedings{icml2025_rethinking_47486,
  title = {{Rethinking Benign Overfitting in Two-Layer Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45109},
}

@inproceedings{icml2026_intrinsic_50154,
  title = {{Intrinsic Gradient Suppression for Label-Noise Prompt Tuning in Vision–Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=9W17xHFtdQ},
}

@inproceedings{icml2026_the_50823,
  title = {{The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=G43CIfmmxh},
}

@inproceedings{icml2026_tpv_50995,
  title = {{TPV: Parameter Perturbations Through the Lens of Test Prediction Variance}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=HskjhAfdY4},
}

@inproceedings{icml2026_active_55115,
  title = {{Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=vgZ66lux3q},
}

@inproceedings{neurips2024_enhancing_71086,
  title = {{Enhancing Robustness of Last Layer Two-Stage Fair Model Corrections}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96128},
}

@inproceedings{neurips2025_the_75023,
  title = {{The Implicit Bias of Structured State Space Models Can Be Poisoned With Clean Labels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=3UaItHVjyE},
}

@inproceedings{neurips2025_from_75572,
  title = {{From Pretraining to Pathology: How Noise Leads to Catastrophic Inheritance in Medical Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=9c8J2C7ajq},
}

@inproceedings{neurips2025_online_76944,
  title = {{Online Strategic Classification With Noise and Partial Feedback}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Oo9KXaM6Mu},
}

@inproceedings{neurips2025_consistency_78677,
  title = {{Consistency of the \$k\_n\$-nearest neighbor rule under adaptive sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=hDDe38BNsT},
}