arXiv Machine Learning August 3, 2026 By Jiaxiang Yi, Miguel A. Bessa

Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties

Read the original on arXiv Machine Learning →

arXiv:2505. 02743v3 Announce Type: replace Abstract: Real-world data contains aleatoric uncertainty - irreducible noise arising from imperfect measurements or from incomplete knowledge about the data generation process.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 4

Uncertainty Estimation using Variance-Gated Distributions

arXiv:2509. 08846v2 Announce Type: replace-cross Abstract: Evaluation of per-sample uncertainty quantification from neural networks is essential for decision-making involving high-risk applications.

By H. Martin Gillis, Isaac Xu, Thomas Trappenberg

arXiv AI

Jun 2

Possibilistic Predictive Uncertainty for Deep Learning

arXiv:2605. 00600v2 Announce Type: replace-cross Abstract: Deep neural networks achieve impressive results across diverse applications, yet their overconfidence on unseen inputs necessitates reliable epistemic uncertainty modeling.

By Yao Ni, Jeremie Houssineau, Yew Soon Ong, Piotr Koniusz

benchmarks

arXiv Machine Learning

Jun 4

Variance-Gated Ensembles: An Epistemic-Aware Framework for Uncertainty Estimation

arXiv:2602. 08142v2 Announce Type: replace Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation.

By H. Martin Gillis, Isaac Xu, Thomas Trappenberg

benchmarks

arXiv AI

Jun 16

Bayesian 3D Steerable CNNs: Enabling Equivariance and Uncertainty Quantification Simultaneously

arXiv:2606. 15479v1 Announce Type: cross Abstract: Steerable convolutional neural networks (Steerable-CNNs) guarantee SE(3)-equivariance by parameterizing kernels as linear combinations of steerable basis functions, but their deterministic nature precludes uncertainty quantification - limiting their use in settings where confidence estimates are essential.

By Abhishek Keripale, Ponkrshnan Thiagarajan, Susanta Ghosh

safety

arXiv Machine Learning

Aug 11

Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators

arXiv:2608. 07630v1 Announce Type: new Abstract: We adapt two classical statistical estimators for quantifying uncertainty to modern deep learning, in order to provide clearer insights into uncertainty attributable to two sources : aleatoric uncertainty, or locally scarce data.

By Pierre Nodet, Thomas George

arXiv Machine Learning

Jun 15

Uncertainty Estimation and Generalization Bounds for Modern Deep Learning

arXiv:2606. 13818v1 Announce Type: new Abstract: This thesis investigates how Bayesian principles can deepen our understanding of modern deep learning systems.

By Luis A. Ortega

Related stories

Uncertainty Estimation using Variance-Gated Distributions

Possibilistic Predictive Uncertainty for Deep Learning

Variance-Gated Ensembles: An Epistemic-Aware Framework for Uncertainty Estimation

Bayesian 3D Steerable CNNs: Enabling Equivariance and Uncertainty Quantification Simultaneously

Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators

Uncertainty Estimation and Generalization Bounds for Modern Deep Learning