Papers

Surveys

Conference

• Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks [NeurIPS2024 - [PyTorch]
• A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications [AISafety Workshop 2020]

Journal

• A survey of uncertainty in deep neural networks [Artificial Intelligence Review 2023] - [GitHub]
• Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation [TMLR2023]
• A Survey on Uncertainty Estimation in Deep Learning Classification Systems from a Bayesian Perspective [ACM2021]
• Ensemble deep learning: A review [Engineering Applications of AI 2021]
• A review of uncertainty quantification in deep learning: Techniques, applications and challenges [Information Fusion 2021]
• Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods [Machine Learning 2021]
• Predictive inference with the jackknife+ [The Annals of Statistics 2021]
• Uncertainty in big data analytics: survey, opportunities, and challenges [Journal of Big Data 2019]

Arxiv

• A System-Level View on Out-of-Distribution Data in Robotics [arXiv2022]
• A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning [arXiv2022]

Theory

Conference

• Exploring and Exploiting Model Uncertainty in Bayesian Optimization [NeurIPS2025]
• A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods [NeurIPS2023]
• Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning [ICLR2023]
• Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? [ICLR2023]
• Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs [ICML2023] - [PyTorch]
• On Second-Order Scoring Rules for Epistemic Uncertainty Quantification [ICML2023]
• Neural Variational Gradient Descent [AABI2022]
• Top-label and multiclass-to-binary reductions [ICLR2022]
• Bayesian Model Selection, the Marginal Likelihood, and Generalization [ICML2022]
• With malice towards none: Assessing uncertainty via equalized coverage [AIES 2021]
• Uncertainty in Gradient Boosting via Ensembles [ICLR2021] - [PyTorch]
• Repulsive Deep Ensembles are Bayesian [NeurIPS2021] - [PyTorch]
• Bayesian Optimization with High-Dimensional Outputs [NeurIPS2021]
• Residual Pathway Priors for Soft Equivariance Constraints [NeurIPS2021]
• Dangers of Bayesian Model Averaging under Covariate Shift [NeurIPS2021] - [TensorFlow]
• A Mathematical Analysis of Learning Loss for Active Learning in Regression [CVPR Workshop2021]
• Why Are Bootstrapped Deep Ensembles Not Better? [NeurIPS Workshop]
• Deep Convolutional Networks as shallow Gaussian Processes [ICLR2019]
• On the accuracy of influence functions for measuring group effects [NeurIPS2018]
• To Trust Or Not To Trust A Classifier [NeurIPS2018] - [Python]
• Understanding Measures of Uncertainty for Adversarial Example Detection [UAI2018]

Journal

• Martingale posterior distributions [Royal Statistical Society Series B]
• A Unified Theory of Diversity in Ensemble Learning [JMLR2023]
• Multivariate Uncertainty in Deep Learning [TNNLS2021]
• A General Framework for Uncertainty Estimation in Deep Learning [RAL2020]
• Adaptive nonparametric confidence sets [Ann. Statist. 2006]

Arxiv

• Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping [arXiv2022]
• Efficient Gaussian Neural Processes for Regression [arXiv2021]
• Dense Uncertainty Estimation [arXiv2021] - [PyTorch]
• A higher-order swiss army infinitesimal jackknife [arXiv2019]

Bayesian-Methods

Conference

• Quantifying Uncertainty in the Presence of Distribution Shifts [NeurIPS2025]
• Training Bayesian Neural Networks with Sparse Subspace Variational Inference [ICLR2024]
• Variational Bayesian Last Layers [ICLR2024]
• A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors [ICLR2024]
• Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation [NeurIPS2023]
• Uncertainty-aware Unsupervised Video Hashing [AISTATS2023] - [PyTorch]
• Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning [CVPR2023]
• Robustness to corruption in pre-trained Bayesian neural networks [ICLR2023]
• Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift [NeurIPS2023] - [PyTorch]
• Transformers Can Do Bayesian Inference [ICLR2022] - [PyTorch]
• Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture [NeurIPS2022]
• On Batch Normalisation for Approximate Bayesian Inference [AABI2021]
• Activation-level uncertainty in deep neural networks [ICLR2021]
• Laplace Redux – Effortless Bayesian Deep Learning [NeurIPS2021] - [PyTorch]
• On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks [UAI2021]
• Learnable uncertainty under Laplace approximations [UAI2021]
• Bayesian Neural Networks with Soft Evidence [ICML Workshop2021] - [PyTorch]
• TRADI: Tracking deep neural network weight distributions for uncertainty estimation [ECCV2020] - [PyTorch]
• How Good is the Bayes Posterior in Deep Neural Networks Really? [ICML2020]
• Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors [[ICML2020]](<http://proceedin