arXiv AI
Jul 15
Quantification of Credal Uncertainty: A Distance-Based Approach
arXiv:2603. 27270v2 Announce Type: replace Abstract: Credal sets, i.
An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
arXiv:2504. 18433v3 Announce Type: replace Abstract: Uncertainty quantification is crucial in machine learning, yet most (axiomatic) studies of uncertainty measures focus on classification, leaving a gap in regression settings with limited formal justification and evaluations.
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arXiv AI
Jul 17
Subjective Risk Decomposition: A New View for Uncertainty Quantification
arXiv:2607. 15196v1 Announce Type: cross Abstract: We present a novel viewpoint for uncertainty quantification.
arXiv Machine Learning
Jun 10
Can we trust our models? Epistemic calibration in second-order classification
arXiv:2606. 10777v1 Announce Type: new Abstract: Uncertainty estimation is critical for deploying machine learning models in high-stakes settings.
arXiv Machine Learning
Jun 19
On the QUEST for Uncertainty Quantification via Highest Density Regions
arXiv:2606. 19569v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is essential for reliable decision-making in safety-critical applications in probabilistic machine learning.
arXiv Machine Learning
Jul 7
Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
arXiv:2510. 25599v2 Announce Type: replace Abstract: Regression tasks, notably in safety-critical domains, require reliable uncertainty quantification, yet the literature remains largely classification-focused.
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.
arXiv Machine Learning
Jun 11
What Uncertainties Do We Need for Dynamical Systems?
arXiv:2606. 11988v1 Announce Type: new Abstract: The distinction between aleatoric and epistemic uncertainty has received considerable attention in machine learning research, mainly in the context of supervised learning but also in other settings such as generative modeling.
arXiv Machine Learning
Sep 4
Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
The review examines how Uncertainty Quantification (UQ) can enhance machine learning applied to biosignals such as EEG, ECG, EOG, and EMG. It surveys 53 papers, outlining current methods, shortcomings, and theoretical frameworks, while highlighting misconceptions and gaps in diagnostic and prosthetic control contexts. The authors recommend further research on human-system interaction with UQ models in clinical settings.
arXiv AI
Jun 26
Decision-Aligned Evaluation of Uncertainty Quantification
arXiv:2606. 26990v1 Announce Type: cross Abstract: Uncertainty estimates in machine learning are typically evaluated using generic metrics such as the negative log-likelihood and expected calibration error, yet good performance on such metrics does not necessarily imply high utility in downstream decisions.
arXiv Machine Learning
Aug 7
A Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies
arXiv:2608. 05995v1 Announce Type: new Abstract: Reliable uncertainty estimates are critical in safety-sensitive applications, where understanding the sources of predictive uncertainty is essential.
arXiv Machine Learning
Jul 7
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
arXiv:2507. 08150v4 Announce Type: replace-cross Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling.
arXiv AI
Jul 17
Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning
arXiv:2607. 14817v1 Announce Type: cross Abstract: Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning.