Pith. sign in

REVIEW 6 cited by

Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1910.09457 v3 pith:ROPKJJG3 submitted 2019-10-21 cs.LG stat.ML

Eyke Hüllermeier , Willem Waegeman This is my paper · ORCID
classification cs.LGstat.ML
keywords learningmachineuncertaintyaleatoricbeenepistemicimportanceintroduction
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often referred to as aleatoric and epistemic. In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Performance Prediction for Large Systems via Text-to-Text Regression

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A 60M-parameter text-to-text regression model predicts Google Borg cluster efficiency from raw system logs, achieving up to 0.99 rank correlation and 100x lower MSE than tabular baselines.

  2. A Unified Evaluation Framework for Epistemic Predictions

    cs.LG 2025-01 conditional novelty 6.0 of 10

    A new metric E = KL distance to the nearest credal-set vertex plus λ times non-specificity ranks uncertainty-aware classifiers under a user-chosen accuracy-precision trade-off.

  3. DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods

    cs.LG 2024-11 conditional novelty 5.0 of 10

    On simple toy data, both DE and DER scale their predicted noise with the injected level, but miscalibration appears in half of DE experiments and nearly all DER experiments.

  4. Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation

    stat.ML 2026-05 conditional novelty 4.0 of 10

    A unified taxonomy of uncertainty in ML for physics is introduced together with validation tools such as coverage, calibration, and proper scoring rules, illustrated on regression and classification tasks.

  5. Uncertainty Quantification for Surface Ozone Emulators using Deep Learning

    cs.LG 2025-08 conditional novelty 4.0 of 10

    An uncertainty-aware U-Net using MC Dropout and Conformalized Quantile Regression estimates MOMO-Chem surface ozone bias, with UQ maps identifying hard-to-correct regions.

  6. Bayesian Data Augmentation and Training for Perception DNN in Autonomous Aerial Vehicles

    cs.RO 2024-12 conditional novelty 4.0 of 10

    A closed-loop simulation and Bayesian optimization framework selects data augmentation hyperparameters for a YOLO landing-pad detector, improving simulated VTOL landing success from 50% to 70%.

Pith tools