% Key papers for Sample selection and co-training (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_generalized_57161,
  title = {{Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11838},
}

@inproceedings{neurips2018_co_56945,
  title = {{Co-teaching: Robust training of deep neural networks with extremely noisy labels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11815},
}

@inproceedings{aaai2017_robust_104517,
  title = {{Robust Loss Functions under Label Noise for Deep Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10894},
}

@inproceedings{cvpr2018_joint_13242,
  title = {{Joint Optimization Framework for Learning With Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Tanaka_Joint_Optimization_Framework_CVPR_2018_paper.html},
}

@inproceedings{cvpr2020_combating_15262,
  title = {{Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{icml2018_mentornet_36846,
  title = {{MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1952},
}

@inproceedings{iclr2017_training_28248,
  title = {{Training deep neural-networks using a noise adaptation layer}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=H12GRgcxg},
}

@inproceedings{cvpr2019_probabilistic_14669,
  title = {{Probabilistic End-To-End Noise Correction for Learning With Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2019_meta_58596,
  title = {{Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13397},
}

@inproceedings{cvpr2019_learning_14443,
  title = {{Learning to Learn From Noisy Labeled Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2020_early_59753,
  title = {{Early-Learning Regularization Prevents Memorization of Noisy Labels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18808},
}

@inproceedings{icml2018_dimensionality_36641,
  title = {{Dimensionality-Driven Learning with Noisy Labels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1970},
}

@inproceedings{cvpr2022_selective_19813,
  title = {{Selective-Supervised Contrastive Learning With Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2017_decoupling_56234,
  title = {{Decoupling "when to update" from "how to update"}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8890},
}

@inproceedings{icml2019_selfie_37711,
  title = {{SELFIE: Refurbishing Unclean Samples for Robust Deep Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3582},
}

@inproceedings{cvpr2021_jo_17217,
  title = {{Jo-SRC: A Contrastive Approach for Combating Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2017_toward_56731,
  title = {{Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9334},
}

@inproceedings{icml2019_how_37428,
  title = {{How does Disagreement Help Generalization against Label Corruption?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3772},
}

@inproceedings{aaai2018_deep_101302,
  title = {{Deep Learning from Crowds}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11506},
}

@inproceedings{cvpr2021_multi_17443,
  title = {{Multi-Objective Interpolation Training for Robustness To Label Noise}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2024_l2b_23872,
  title = {{L2B: Learning to Bootstrap Robust Models for Combating Label Noise}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31258},
}

@inproceedings{cvpr2024_learning_23993,
  title = {{Learning with Structural Labels for Learning with Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30859},
}

@inproceedings{aaai2024_dirichlet_5701,
  title = {{Dirichlet-Based Prediction Calibration for Learning with Noisy Labels}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29672},
}

@inproceedings{neurips2024_curriculum_74364,
  title = {{Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93198},
}

@inproceedings{cvpr2025_directional_25953,
  title = {{Directional Label Diffusion Model for Learning from Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33218},
}

@inproceedings{aaai2025_combating_8462,
  title = {{Combating Semantic Contamination in Learning with Label Noise}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32293},
}

@inproceedings{aaai2024_iofm_6253,
  title = {{IOFM: Using the Interpolation Technique on the Over-Fitted Models to Identify Clean-Annotated Samples}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29209},
}

@inproceedings{aaai2025_enhancing_8981,
  title = {{Enhancing Noise-Robust Losses for Large-Scale Noisy Data Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32752},
}

@inproceedings{icml2024_pi_43658,
  title = {{Pi-DUAL: Using privileged information to distinguish clean from noisy labels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34794},
}

@inproceedings{icml2025_on_48387,
  title = {{On the Role of Label Noise in the Feature Learning Process}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44229},
}

@inproceedings{neurips2024_epsilon_74373,
  title = {{\$epsilon\$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93191},
}

@inproceedings{iclr2026_trapped_88477,
  title = {{Trapped by simplicity: When Transformers fail to learn from noisy features}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=n5bPL58uMC},
}

@inproceedings{iclr2024_robust_35588,
  title = {{Robust Classification via Regression for Learning with Noisy Labels}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17485},
}

@inproceedings{iclr2026_enhancing_87838,
  title = {{Enhancing Learning with Noisy Labels via Rockafellian Relaxation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=g4EpGiN5X3},
}

@inproceedings{neurips2026_readiness_92794,
  title = {{Readiness-Aware Sample Selection for Noisy Labels with Class Imbalance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/153563},
}