% Key papers for Crowdsourced label aggregation (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2018_learning_28500,
  title = {{Learning From Noisy Singly-labeled Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/158},
}

@inproceedings{aaai2021_learning_905,
  title = {{Learning from Crowds by Modeling Common Confusions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/05832-learning-from-crowds-by-modeling-common-confusions/},
}

@inproceedings{icml2019_exploiting_37344,
  title = {{Exploiting Worker Correlation for Label Aggregation in Crowdsourcing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4013},
}

@inproceedings{aaai2024_dual_5757,
  title = {{Dual Self-Paced Cross-Modal Hashing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29441},
}

@inproceedings{icml2016_no_35916,
  title = {{No Oops, You Won’t Do It Again: Mechanisms for Self-correction in Crowdsourcing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/shaha16.html},
}

@inproceedings{aaai2024_coupled_5519,
  title = {{Coupled Confusion Correction: Learning from Crowds with Sparse Annotations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29613},
}

@inproceedings{aaai2016_learning_104980,
  title = {{Learning with Marginalized Corrupted Features and Labels Together}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10152},
}

@inproceedings{icml2017_learning_36265,
  title = {{Learning to Aggregate Ordinal Labels by Maximizing Separating Width}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/503},
}

@inproceedings{iclr2020_discrepancy_29367,
  title = {{Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=Byg-wJSYDS},
}

@inproceedings{neurips2022_cs_64617,
  title = {{CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53147},
}

@inproceedings{iclr2023_deep_32805,
  title = {{Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/10918},
}

@inproceedings{icml2021_pointwise_39787,
  title = {{Pointwise Binary Classification with Pairwise Confidence Comparisons}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9839},
}

@inproceedings{neurips2023_a_68323,
  title = {{A Path to Simpler Models Starts With Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71482},
}

@inproceedings{aaai2025_label_9693,
  title = {{Label Aggregation for Composite Crowd Tasks by Worker Ability Constraint Satisfaction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33564},
}

@inproceedings{neurips2023_capro_66646,
  title = {{CAPro: Webly Supervised Learning with Cross-modality Aligned Prototypes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72979},
}

@inproceedings{neurips2024_iwbvt_72836,
  title = {{IWBVT: Instance Weighting-based Bias-Variance Trade-off for Crowdsourcing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94564},
}

@inproceedings{neurips2024_kfnn_74450,
  title = {{KFNN: K-Free Nearest Neighbor For Crowdsourcing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93123},
}

@inproceedings{icml2025_bipartite_48212,
  title = {{Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44399},
}

@inproceedings{neurips2023_label_69393,
  title = {{Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70516},
}

@inproceedings{neurips2023_calibrating_69982,
  title = {{Calibrating “Cheap Signals” in Peer Review without a Prior}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/69984},
}

@inproceedings{icml2024_unbiased_44825,
  title = {{Unbiased Multi-Label Learning from Crowdsourced Annotations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33627},
}

@inproceedings{icml2026_ma_52786,
  title = {{MA\$^3\$S: Model-Agnostic Active Annotation Strategy for Crowdsourcing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Z3WwECYvgL},
}

@inproceedings{iclr2026_towards_85500,
  title = {{Towards a Foundation Model for Crowdsourced Label Aggregation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=FF9QVQduAu},
}