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

@inproceedings{cvpr2020_luvli_15733,
  title = {{LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{icml2020_ngboost_38488,
  title = {{NGBoost: Natural Gradient Boosting for Probabilistic Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6336},
}

@inproceedings{cvpr2023_learning_21429,
  title = {{Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22787},
}

@inproceedings{aaai2020_maximizing_101987,
  title = {{Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5849},
}

@inproceedings{cvpr2022_deep_18610,
  title = {{Deep Depth From Focus With Differential Focus Volume}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{iclr2021_uncertainty_29964,
  title = {{Uncertainty in Gradient Boosting via Ensembles}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2928},
}

@inproceedings{neurips2018_dirichlet_57050,
  title = {{Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11583},
}

@inproceedings{neurips2022_card_63861,
  title = {{CARD: Classification and Regression Diffusion Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52972},
}

@inproceedings{neurips2019_accurate_57863,
  title = {{Accurate Uncertainty Estimation and Decomposition in Ensemble Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13943},
}

@inproceedings{neurips2016_disco_55659,
  title = {{DISCO Nets : DISsimilarity COefficients Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/8502},
}

@inproceedings{aaai2021_capturing_232,
  title = {{Capturing Uncertainty in Unsupervised GPS Trajectory Segmentation Using Bayesian Deep Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/00390-capturing-uncertainty-in-unsupervised-gps-trajectory-segmentation-using-bayesian-deep-learning/},
}

@inproceedings{cvpr2018_large_13257,
  title = {{Large-Scale Distance Metric Learning With Uncertainty}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Qian_Large-Scale_Distance_Metric_CVPR_2018_paper.html},
}

@inproceedings{neurips2020_introducing_60106,
  title = {{Introducing Routing Uncertainty in Capsule Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17242},
}

@inproceedings{icml2018_constraining_36593,
  title = {{Constraining the Dynamics of Deep Probabilistic Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2209},
}

@inproceedings{iclr2023_packed_32733,
  title = {{Packed Ensembles for efficient uncertainty estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11934},
}

@inproceedings{cvpr2023_revisiting_22061,
  title = {{Revisiting Rotation Averaging: Uncertainties and Robust Losses}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21851},
}

@inproceedings{cvpr2020_scalable_16180,
  title = {{Scalable Uncertainty for Computer Vision With Functional Variational Inference}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2023_learning_21354,
  title = {{Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22857},
}

@inproceedings{iclr2019_feed_29060,
  title = {{Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1122},
}

@inproceedings{iclr2020_hypermodels_29904,
  title = {{Hypermodels for Exploration}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=ryx6WgStPB},
}

@inproceedings{cvpr2024_quantifying_24552,
  title = {{Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31518},
}

@inproceedings{icml2024_variational_44862,
  title = {{Variational Learning is Effective for Large Deep Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33590},
}

@inproceedings{neurips2024_kermut_73513,
  title = {{Kermut: Composite kernel regression for protein variant effects}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93963},
}

@inproceedings{aaai2025_auto_8186,
  title = {{Auto Encoding Neural Process for Multi-interest Recommendation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33293},
}

@inproceedings{neurips2024_wasserstein_73051,
  title = {{Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94373},
}

@inproceedings{aaai2026_physics_100092,
  title = {{Physics-Informed Multi-Task Learning for Battery State of Health Prediction with Uncertainty Quantification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37143},
}

@inproceedings{iclr2024_on_34712,
  title = {{On the Posterior Distribution in Denoising: Application to Uncertainty Quantification}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18362},
}

@inproceedings{icml2024_enabling_44239,
  title = {{Enabling Uncertainty Estimation in Iterative Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34213},
}

@inproceedings{icml2024_density_45250,
  title = {{Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33202},
}

@inproceedings{icml2024_a_45818,
  title = {{A Rate-Distortion View of Uncertainty Quantification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32634},
}

@inproceedings{neurips2024_combining_74506,
  title = {{Combining Statistical Depth and Fermat Distance for Uncertainty Quantification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93070},
}

@inproceedings{neurips2025_distance_76553,
  title = {{Distance-informed Neural Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=KYb3sw847W},
}

@inproceedings{iclr2024_fast_34262,
  title = {{Fast Ensembling with Diffusion Schrödinger Bridge}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18813},
}

@inproceedings{icml2024_uncertainty_43717,
  title = {{Uncertainty for Active Learning on Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34735},
}

@inproceedings{icml2024_orthogonal_43984,
  title = {{Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34468},
}

@inproceedings{icml2024_a_44387,
  title = {{A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34065},
}

@inproceedings{icml2024_partially_45140,
  title = {{Partially Stochastic Infinitely Deep Bayesian Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33312},
}

@inproceedings{icml2025_can_46378,
  title = {{Can We Predict Performance of Large Models across Vision-Language Tasks?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46185},
}

@inproceedings{icml2026_scheduling_51014,
  title = {{Scheduling LLM Inference with Uncertainty-Aware Output Length Predictions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=I5IMkvVKd7},
}

@inproceedings{icml2026_large_51933,
  title = {{Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=REezhltCeh},
}