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

@inproceedings{icml2018_high_36738,
  title = {{High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2188},
}

@inproceedings{cvpr2019_learning_14431,
  title = {{Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{aaai2024_robust_7179,
  title = {{Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30084},
}

@inproceedings{neurips2016_testing_56035,
  title = {{Testing for Differences in Gaussian Graphical Models:  Applications to Brain Connectivity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7209},
}

@inproceedings{neurips2022_training_64758,
  title = {{Training Uncertainty-Aware Classifiers with Conformalized Deep Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54954},
}

@inproceedings{neurips2021_beyond_62182,
  title = {{Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27027},
}

@inproceedings{icml2020_discriminative_38109,
  title = {{Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6179},
}

@inproceedings{icml2022_image_40672,
  title = {{Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16833},
}

@inproceedings{aaai2021_learning_892,
  title = {{Learning Prediction Intervals for Model Performance}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07305-learning-prediction-intervals-for-model-performance/},
}

@inproceedings{neurips2020_confidence_59567,
  title = {{Confidence sequences for sampling without replacement}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18900},
}

@inproceedings{icml2019_distribution_37305,
  title = {{Distribution calibration for regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4047},
}

@inproceedings{neurips2024_neural_74644,
  title = {{Neural Conditional Probability for Uncertainty Quantification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92949},
}

@inproceedings{aaai2025_combining_8463,
  title = {{Combining Priors with Experience: Confidence Calibration Based on Binomial Process Modeling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33792},
}

@inproceedings{icml2022_calibrated_40316,
  title = {{Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17391},
}

@inproceedings{neurips2021_locally_63535,
  title = {{Locally Valid and Discriminative Prediction Intervals for Deep Learning Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27965},
}

@inproceedings{aaai2026_beta_108830,
  title = {{Beta Distribution Learning for Reliable Roadway Crash Risk Assessment}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/41182},
}

@inproceedings{icml2020_on_38541,
  title = {{On Semi-parametric Inference for BART}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6511},
}

@inproceedings{neurips2022_jaws_65024,
  title = {{JAWS: Auditing Predictive Uncertainty Under Covariate Shift}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52881},
}

@inproceedings{neurips2023_efficient_66914,
  title = {{Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72732},
}

@inproceedings{neurips2024_treeffuser_70427,
  title = {{Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96678},
}

@inproceedings{neurips2024_score_71598,
  title = {{Score-based generative models are provably robust: an uncertainty quantification perspective}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95673},
}

@inproceedings{neurips2024_transductive_74230,
  title = {{Transductive Active Learning: Theory and Applications}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93324},
}

@inproceedings{aaai2025_personalizedus_10291,
  title = {{PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35017},
}

@inproceedings{iclr2024_uncertainty_34783,
  title = {{Uncertainty Quantification via Stable Distribution Propagation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18291},
}

@inproceedings{icml2026_principled_52897,
  title = {{Principled Confidence Estimation for Deep Computed Tomography}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=a6vCNSBBeq},
}

@inproceedings{icml2026_nonparametric_53433,
  title = {{Nonparametric Distribution Regression Re-calibration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=fTl7NXYtAB},
}

@inproceedings{neurips2024_non_72166,
  title = {{Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95172},
}

@inproceedings{neurips2025_uncertainty_77936,
  title = {{Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=ZL7RuWd6QK},
}

@inproceedings{neurips2025_bernstein_78081,
  title = {{Bernstein–von Mises for Adaptively Collected Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=arw2uk9Ica},
}

@inproceedings{neurips2025_statistical_78596,
  title = {{Statistical Inference for Gradient Boosting Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=gLU0UV85Kv},
}

@inproceedings{neurips2025_smooth_79092,
  title = {{Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Associations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=lTP1RxTFSX},
}

@inproceedings{neurips2025_credal_79627,
  title = {{Credal Prediction based on Relative Likelihood}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=rKM3oqruN3},
}

@inproceedings{iclr2025_conformalized_82549,
  title = {{Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29247},
}

@inproceedings{iclr2026_efficient_85218,
  title = {{Efficient Credal Prediction through Decalibration}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=BqOmsYIe7M},
}

@inproceedings{neurips2026_proper_90665,
  title = {{Proper Scoring Rules for Agentic Uncertainty Quantification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155086},
}