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

@inproceedings{icml2016_dropout_35805,
  title = {{Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/gal16.html},
}

@inproceedings{neurips2019_can_57975,
  title = {{Can you trust your model's uncertainty?  Evaluating predictive uncertainty under dataset shift}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14325},
}

@inproceedings{neurips2017_simple_56669,
  title = {{Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9410},
}

@inproceedings{neurips2018_evidential_57104,
  title = {{Evidential Deep Learning to Quantify Classification Uncertainty}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11322},
}

@inproceedings{neurips2018_predictive_57527,
  title = {{Predictive Uncertainty Estimation via Prior Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11679},
}

@inproceedings{icml2020_uncertainty_38893,
  title = {{Uncertainty Estimation Using a Single Deep Deterministic Neural Network}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6512},
}

@inproceedings{icml2018_bayesian_36542,
  title = {{Bayesian Uncertainty Estimation for Batch Normalized Deep Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2039},
}

@inproceedings{icml2018_decomposition_36615,
  title = {{Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2262},
}

@inproceedings{icml2017_dropout_36164,
  title = {{Dropout Inference in Bayesian Neural Networks with Alpha-divergences}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/539},
}

@inproceedings{neurips2020_simple_60829,
  title = {{Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17741},
}

@inproceedings{cvpr2021_masksembles_17391,
  title = {{Masksembles for Uncertainty Estimation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2019_bayesian_57940,
  title = {{Bayesian Layers: A Module for Neural Network Uncertainty}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14379},
}

@inproceedings{neurips2019_single_58985,
  title = {{Single-Model Uncertainties for Deep Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13754},
}

@inproceedings{neurips2020_posterior_60595,
  title = {{Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18555},
}

@inproceedings{aaai2020_uncertainty_102153,
  title = {{Uncertainty-Aware Deep Classifiers Using Generative Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6015},
}

@inproceedings{icml2020_sde_38719,
  title = {{SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6065},
}

@inproceedings{iclr2022_on_31444,
  title = {{On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6755},
}

@inproceedings{iclr2019_bias_29039,
  title = {{Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/963},
}

@inproceedings{neurips2020_improving_60072,
  title = {{Improving model calibration with accuracy versus uncertainty optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16799},
}

@inproceedings{neurips2021_uncertainty_63491,
  title = {{Uncertainty Quantification and Deep Ensembles}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27586},
}

@inproceedings{aaai2024_deep_5634,
  title = {{Deep Active Learning with Noise Stability}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29270},
}

@inproceedings{cvpr2024_make_24098,
  title = {{Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30948},
}

@inproceedings{aaai2024_discretization_5709,
  title = {{Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28508},
}

@inproceedings{cvpr2025_uncertainty_27987,
  title = {{Uncertainty Weighted Gradients for Model Calibration}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32877},
}

@inproceedings{aaai2025_general_9312,
  title = {{General Uncertainty Estimation with Delta Variances}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34238},
}

@inproceedings{neurips2025_knowledge_76219,
  title = {{Knowledge Distillation of Uncertainty using Deep Latent Factor Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Grpqzwpwv8},
}

@inproceedings{neurips2025_torch_79367,
  title = {{Torch-Uncertainty: Deep Learning Uncertainty Quantification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=oYfRRQr9uK},
}

@inproceedings{icml2024_measuring_44070,
  title = {{Measuring Stochastic Data Complexity with Boltzmann Influence Functions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34382},
}

@inproceedings{icml2025_fully_46270,
  title = {{Fully Heteroscedastic Count Regression with Deep Double Poisson Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46290},
}

@inproceedings{icml2026_possibilistic_51565,
  title = {{Possibilistic Predictive Uncertainty for Deep Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=NhL0mBNFk4},
}

@inproceedings{neurips2025_quantifying_74684,
  title = {{Quantifying Uncertainty in the Presence of Distribution Shifts}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=04p7u1gIsv},
}

@inproceedings{neurips2025_uncertainty_76252,
  title = {{Uncertainty Quantification for Physics-Informed Neural Networks with Extended Fiducial Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=HFcQGutJJn},
}

@inproceedings{neurips2025_uncertainty_77242,
  title = {{Uncertainty Quantification with the Empirical Neural Tangent Kernel}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Ro1a0MTRq5},
}

@inproceedings{iclr2025_distribution_83507,
  title = {{Distribution-Free Data Uncertainty for Neural Network Regression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28309},
}

@inproceedings{iclr2025_hierarchical_83955,
  title = {{Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27869},
}

@inproceedings{neurips2026_deep_94380,
  title = {{Deep Heteroskedastic Regression: Post-Hoc Variance Estimation from Latent Representations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/151494},
}