REVIEW 3 cited by
Second-Order Uncertainty Quantification: Variance-Based Measures
Not yet reviewed by Pith; the record is open.
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
Second-Order Uncertainty Quantification: Variance-Based Measures
read the original abstract
Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process in real-world applications. This paper proposes a novel way to use variance-based measures to quantify uncertainty on the basis of second-order distributions in classification problems. A distinctive feature of the measures is the ability to reason about uncertainties on a class-based level, which is useful in situations where nuanced decision-making is required. Recalling some properties from the literature, we highlight that the variance-based measures satisfy important (axiomatic) properties. In addition to this axiomatic approach, we present empirical results showing the measures to be effective and competitive to commonly used entropy-based measures.
Forward citations
Cited by 3 Pith papers
-
Subjective Risk Decomposition: A New View for Uncertainty Quantification
Most existing epistemic/aleatoric uncertainty measures are special cases of one bias-variance-entropy decomposition of subjective risk under a strictly proper loss.
-
Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
Kernel-score divergences define a unified family of regression uncertainty measures whose kernel choice controls robustness, tail sensitivity, and OOD responsiveness.
-
Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned
The paper presents the TÜV AUSTRIA Trusted AI audit catalog, a statistical framework based on the Stochastic Application Domain Definition, minimum performance requirements, and independent-sample testing for certifyi...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.