arXiv Machine Learning July 30, 2026 By Tanmoy Mukherjee, Marius Kloft, Pierre Marquis, Zied Bouraoui

Structurally Separated Uncertainty in Supervised Latent Variable Models

Read the original on arXiv Machine Learning →

arXiv:2602. 11219v2 Announce Type: replace Abstract: Predictive uncertainty is commonly decomposed into epistemic and aleatoric components, but standard decompositions often produce strongly correlated estimates because both quantities are derived from the same predictive distribution.

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

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

Aug 12

ELVAE: Evidential Learning-Based Variational Autoencoder for Uncertainty-Aware Generation

arXiv:2608. 10398v1 Announce Type: cross Abstract: Variational autoencoders generate samples from probabilistic latent representations but do not distinguish uncertainty about the latent location from variability around it.

By Ge Wang

arXiv Machine Learning

Jun 19

Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence

arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.

By Jinseok Chung, Minkyoung Song, Hyunji Jung, Namhoon Lee

llms benchmarks safety

arXiv Machine Learning

Aug 3

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

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.

By Jiaxiang Yi, Miguel A. Bessa

arXiv AI

Jul 3

An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms

arXiv:2603. 29466v2 Announce Type: replace-cross Abstract: Existing methods for quantifying predictive uncertainty in neural networks are either computationally intractable for large language models or require access to training data that is typically unavailable.

By Nils Gr\"unefeld, Jes Frellsen, Christian Hardmeier

llms nlp benchmarks

arXiv Machine Learning

Jun 29

On the Effect of Uncertainty on Layer-wise Inference Dynamics

arXiv:2507. 06722v2 Announce Type: replace-cross Abstract: Understanding how large language models (LLMs) internally represent and process their predictions is central to detecting uncertainty and preventing hallucinations.

By Sunwoo Kim, Haneul Yoo, Alice Oh

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

ELVAE: Evidential Learning-Based Variational Autoencoder for Uncertainty-Aware Generation

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