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

@inproceedings{neurips2017_what_56773,
  title = {{What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9332},
}

@inproceedings{aaai2020_crowd_103745,
  title = {{Crowd Counting with Decomposed Uncertainty}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6852},
}

@inproceedings{neurips2019_cxplain_57970,
  title = {{CXPlain: Causal Explanations for Model Interpretation under Uncertainty}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14040},
}

@inproceedings{cvpr2021_learning_17293,
  title = {{Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{iclr2021_getting_30397,
  title = {{Getting a CLUE: A Method for Explaining Uncertainty Estimates}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2741},
}

@inproceedings{aaai2023_the_4811,
  title = {{The Unreasonable Effectiveness of Deep Evidential Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26096},
}

@inproceedings{iclr2021_uncertainty_30567,
  title = {{Uncertainty Estimation in Autoregressive Structured Prediction}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3015},
}

@inproceedings{aaai2022_improving_2234,
  title = {{Improving Evidential Deep Learning via Multi-Task Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/07895-improving-evidential-deep-learning-via-multi-task-learning/},
}

@inproceedings{neurips2021_reliable_62493,
  title = {{Reliable Post hoc Explanations: Modeling Uncertainty in Explainability}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27871},
}

@inproceedings{neurips2024_estimating_72535,
  title = {{Estimating Epistemic and Aleatoric Uncertainty with a Single Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94833},
}

@inproceedings{aaai2022_diverse_1994,
  title = {{Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/07390-diverse-global-and-amortised-counterfactual-explanations-for-uncertainty-estimates/},
}

@inproceedings{aaai2023_monitoring_4183,
  title = {{Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26755},
}

@inproceedings{neurips2020_deep_59652,
  title = {{Deep Evidential Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18894},
}

@inproceedings{aaai2017_estimating_104572,
  title = {{Estimating Uncertainty Online Against an Adversary}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10949},
}

@inproceedings{neurips2022_pitfalls_65575,
  title = {{Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52837},
}

@inproceedings{neurips2024_benchmarking_74475,
  title = {{Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97444},
}

@inproceedings{neurips2022_on_66017,
  title = {{On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52923},
}

@inproceedings{aaai2024_one_6843,
  title = {{One Step Closer to Unbiased Aleatoric Uncertainty Estimation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29627},
}

@inproceedings{icml2024_using_44998,
  title = {{Using AI Uncertainty Quantification to Improve Human Decision-Making}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33454},
}

@inproceedings{aaai2023_normalizing_4255,
  title = {{Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25834},
}

@inproceedings{neurips2024_are_71982,
  title = {{Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95329},
}

@inproceedings{aaai2025_adaptive_8042,
  title = {{Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34152},
}

@inproceedings{aaai2025_things_10992,
  title = {{Things Machine Learning Models Know That They Don’t Know}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35094},
}

@inproceedings{icml2026_cooperative_50307,
  title = {{Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=B5umYh4CGr},
}

@inproceedings{iclr2025_from_82758,
  title = {{From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29041},
}

@inproceedings{aaai2025_epistemic_9002,
  title = {{Epistemic Bellman Operators}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35393},
}

@inproceedings{icml2024_experts_43686,
  title = {{Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34766},
}

@inproceedings{icml2024_is_45304,
  title = {{Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33148},
}

@inproceedings{neurips2025_rethinking_80217,
  title = {{Rethinking Approximate Gaussian Inference in Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=xPQyRmZ8hz},
}

@inproceedings{aaai2025_repeat_10440,
  title = {{REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32900},
}

@inproceedings{cvpr2024_epistemic_23383,
  title = {{Epistemic Uncertainty Quantification For Pre-Trained Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30735},
}

@inproceedings{icml2024_on_44422,
  title = {{On the Independence Assumption in Neurosymbolic Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34030},
}

@inproceedings{icml2024_can_44495,
  title = {{Can Machines Learn the True Probabilities?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33957},
}

@inproceedings{icml2024_second_44534,
  title = {{Second-Order Uncertainty Quantification: A Distance-Based Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33918},
}

@inproceedings{icml2024_beyond_44693,
  title = {{Beyond the Norms: Detecting Prediction Errors in Regression Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33759},
}

@inproceedings{icml2025_rethinking_46510,
  title = {{Rethinking Aleatoric and Epistemic Uncertainty}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46057},
}