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

@inproceedings{neurips2019_verified_59184,
  title = {{Verified Uncertainty Calibration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14517},
}

@inproceedings{cvpr2023_object_21737,
  title = {{Object Pose Estimation With Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/23288},
}

@inproceedings{iclr2021_uncertainty_30500,
  title = {{Uncertainty Sets for Image Classifiers using Conformal Prediction}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3246},
}

@inproceedings{iclr2020_pac_29322,
  title = {{PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BJxVI04YvB},
}

@inproceedings{icml2020_individual_38315,
  title = {{Individual Calibration with Randomized Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6546},
}

@inproceedings{neurips2020_certifiably_59521,
  title = {{Certifiably Adversarially Robust Detection of Out-of-Distribution Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18869},
}

@inproceedings{neurips2022_better_64856,
  title = {{Better Uncertainty Calibration via Proper Scores for Classification and Beyond}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53262},
}

@inproceedings{aaai2020_model_101377,
  title = {{Model-Based Diagnosis with Uncertain Observations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5664},
}

@inproceedings{aaai2023_trustworthy_4882,
  title = {{Trustworthy Residual Vehicle Value Prediction for Auto Finance}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26842},
}

@inproceedings{icml2023_flexible_41470,
  title = {{Flexible Model Aggregation for Quantile Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/25675},
}

@inproceedings{iclr2021_pac_30297,
  title = {{PAC Confidence Predictions for Deep Neural Network Classifiers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3264},
}

@inproceedings{icml2021_distribution_39261,
  title = {{Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10205},
}

@inproceedings{neurips2023_human_67535,
  title = {{Human-Aligned Calibration for AI-Assisted Decision Making}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72203},
}

@inproceedings{iclr2022_pac_31034,
  title = {{PAC Prediction Sets Under Covariate Shift}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6314},
}

@inproceedings{neurips2021_improving_63201,
  title = {{Improving Conditional Coverage via Orthogonal Quantile Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28405},
}

@inproceedings{icml2020_online_38568,
  title = {{Online Control of the False Coverage Rate and False Sign Rate}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6560},
}

@inproceedings{neurips2021_calibrating_62927,
  title = {{Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27249},
}

@inproceedings{neurips2024_active_70452,
  title = {{Active, anytime-valid risk controlling prediction sets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96655},
}

@inproceedings{neurips2022_censored_66028,
  title = {{Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55198},
}

@inproceedings{iclr2023_taking_33200,
  title = {{Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11048},
}

@inproceedings{icml2024_relaxed_44140,
  title = {{Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34312},
}

@inproceedings{neurips2024_localized_73252,
  title = {{Localized Adaptive Risk Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94186},
}

@inproceedings{aaai2026_coin_108315,
  title = {{COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40667},
}

@inproceedings{icml2024_conformalized_45166,
  title = {{Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33286},
}

@inproceedings{icml2025_safer_46239,
  title = {{SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46321},
}

@inproceedings{icml2025_generalized_48379,
  title = {{Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44237},
}

@inproceedings{neurips2024_disentangling_72544,
  title = {{Disentangling Linear Quadratic Control with Untrusted ML Predictions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94827},
}

@inproceedings{neurips2024_truthfulness_72984,
  title = {{Truthfulness of Calibration Measures}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94433},
}

@inproceedings{neurips2025_a_76697,
  title = {{A Theory for Worst-Case vs. Average-Case Guarantees for LLMs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=M83RPhdsX4},
}

@inproceedings{neurips2025_performative_80149,
  title = {{Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=wiWNpjcYWH},
}

@inproceedings{iclr2025_reassessing_82463,
  title = {{Reassessing How to Compare and Improve the Calibration of Machine Learning Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29330},
}

@inproceedings{neurips2026_conformal_90889,
  title = {{Conformal Selective Acting: Anytime-Valid Risk Control for RLVR-Trained LLMs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/148731},
}

@inproceedings{neurips2026_trust_91628,
  title = {{Trust Guided Decision Transformer}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/151852},
}

@inproceedings{neurips2026_the_91951,
  title = {{The Minimax Rate of Second-Order Calibration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/154263},
}

@inproceedings{neurips2026_qutcc_93573,
  title = {{QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150551},
}

@inproceedings{neurips2026_persuasive_93861,
  title = {{Persuasive Prediction via Decision Calibration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/152558},
}

@inproceedings{neurips2026_testable_94626,
  title = {{Testable and Actionable Calibration for Full Swap Regret}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/oral/148312},
}

@inproceedings{icml2026_care_53866,
  title = {{CARE: Adaptive Calibration for Reliable Recommendations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=jXt84IgAgg},
}

@inproceedings{iclr2025_approximating_83919,
  title = {{Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27902},
}