Paper Citation Record · LEDGER

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression

As of 16 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 6 inbound Pith citation observations for arXiv:2504.18433.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.18433 v3

Coverage vector

measured 35 of 35 reference resolution

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:33:37.760163Z

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A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T00:59:19.948343Z

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

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Outbound references

Observation f7666495-6fb9-4cdb-a9b9-ab8a88c87ec8 · outbound

This paper cites Deep evidential regression.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Deep evidential regression

Reference 1

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Observation 60a4dbc7-24cc-40b1-a4f0-cb8cd1534c28 · outbound

This paper cites Sufficiency and exponential families for discrete sample spaces.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Sufficiency and exponential families for discrete sample spaces

Reference 2

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Observation 62f8e67e-6707-41ad-ad07-21827904a14d · outbound

This paper cites Pitfalls of epistemic uncertainty quantification through loss minimisation.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Pitfalls of epistemic uncertainty quantification through loss minimisation

Reference 3

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Observation ca540e0a-4820-41db-b283-73057f9f8257 · outbound

This paper cites Efficient epistemic uncertainty estimation in regression ensemble models using pairwise-distance estimators.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Efficient epistemic uncertainty estimation in regression ensemble models using pairwise-distance estimators

Reference 4

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Observation 0e4eb4de-67b4-4976-a266-c537ffa17c14 · outbound

This paper cites Axioms for uncertainty measures on belief functions and credal sets.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Axioms for uncertainty measures on belief functions and credal sets

Reference 5

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Observation b5866a75-0633-4934-8162-51ba7aded8be · outbound

This paper cites Uncertainty quantification for data-driven weather models.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Uncertainty quantification for data-driven weather models

Reference 6

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Observation 8b137d9d-2c65-4a6e-bea6-3563fcfd767e · outbound

This paper cites Disentangling epistemic and aleatoric uncertainty in reinforcement learning, 2022.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Disentangling epistemic and aleatoric uncertainty in reinforcement learning, 2022

Reference 7

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An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Unresolved cited work

Reference 8

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This paper cites Uncertainty quantification in deep mri reconstruction.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Uncertainty quantification in deep mri reconstruction

Reference 9

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Observation bf976344-d3c7-46ca-b14d-da157965639a · outbound

This paper cites Quantifying aleatoric and epistemic uncertainty: A credal approach.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Quantifying aleatoric and epistemic uncertainty: A credal approach

Reference 10

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Observation fe9f4a6f-c2a0-4f99-b3f6-5a555153e4d6 · outbound

This paper cites Bayesian Active Learning for Classification and Preference Learning.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Bayesian Active Learning for Classification and Preference Learning

Reference 11

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Observation f251bfa0-8b81-4cdf-987a-397a0d32103e · outbound

This paper cites Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods

Reference 12

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This paper cites Quantification of credal uncertainty in machine learning: A critical analysis and empirical comparison.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Quantification of credal uncertainty in machine learning: A critical analysis and empirical comparison

Reference 13

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Observation 7f5696c9-5f79-43af-8ff5-cdce991cf9fd · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017

Reference 14

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Observation 1b59d3d1-67cf-401b-a8bd-e60c20c5c44c · outbound

This paper cites Rage against the mean--a review of distributional regression approaches.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Rage against the mean--a review of distributional regression approaches

Reference 15

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Observation da7f185c-4bc5-49dd-b18b-fbdc2c46637e · outbound

This paper cites Deup: Direct epistemic uncertainty prediction, 2023.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Deup: Direct epistemic uncertainty prediction, 2023

Reference 16

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Observation 26a22ad0-2112-419b-841f-71abbe0b219a · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 17

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Observation 52659851-c5d7-4c7a-9394-d7eb16aa3aec · outbound

This paper cites Fast, Lüder A.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Fast, Lüder A

Reference 18

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This paper cites The enterprise of knowledge: An essay on knowledge, credal probability, and chance.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression The enterprise of knowledge: An essay on knowledge, credal probability, and chance

Reference 19

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An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression o hr, Michael Ingrisch, and Eyke H \

Reference 20

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This paper cites Regression prior networks, 2020.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Regression prior networks, 2020

Reference 21

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Observation ec217fb3-5d6e-489a-80d4-b61dd63eabee · outbound

This paper cites Multivariate deep evidential regression, 2022.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Multivariate deep evidential regression, 2022

Reference 22

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Observation 7b265df9-2e82-4f30-826a-78c07e0faf08 · outbound

This paper cites Evaluating uncertainty quantification in end-to-end autonomous driving control, 2018.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Evaluating uncertainty quantification in end-to-end autonomous driving control, 2018

Reference 23

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Observation 1792d041-fc56-4fe7-a98e-d9c6192f4670 · outbound

This paper cites Multi-horizon wind power forecasting using multi-modal spatio-temporal neural networks.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Multi-horizon wind power forecasting using multi-modal spatio-temporal neural networks

Reference 24

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This paper cites Entropies and cross-entropies of exponential families.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Entropies and cross-entropies of exponential families

Reference 25

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Observation e67c38c8-1d65-4ce7-98ed-30f23e98e800 · outbound

This paper cites Uncertainty measures for evidential reasoning ii: A new measure of total uncertainty.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Uncertainty measures for evidential reasoning ii: A new measure of total uncertainty

Reference 26

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Observation c8789d4d-02fa-4b60-a90d-6d86951dd578 · outbound

This paper cites The hidden uncertainty in a neural networks activations, 2021.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression The hidden uncertainty in a neural networks activations, 2021

Reference 27

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Observation f72ad3a9-61b8-4f9a-85c2-d34c84e4c2f6 · outbound

This paper cites Neural networks for postprocessing ensemble weather forecasts.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Neural networks for postprocessing ensemble weather forecasts

Reference 28

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This paper cites Second-order uncertainty quantification: A distance-based approach.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Second-order uncertainty quantification: A distance-based approach

Reference 29

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Observation cd249356-cbff-43d3-afd2-1cda6adafcca · outbound

This paper cites Is the volume of a credal set a good measure for epistemic uncertainty? In Uncertainty in Artificial Intelligence, pages 1795--1804.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Is the volume of a credal set a good measure for epistemic uncertainty? In Uncertainty in Artificial Intelligence, pages 1795--1804

Reference 30

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This paper cites Label-wise Aleatoric and Epistemic Uncertainty Quantification.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Label-wise Aleatoric and Epistemic Uncertainty Quantification

Reference 31

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This paper cites Evaluation of uncertainty quantification in deep learning.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Evaluation of uncertainty quantification in deep learning

Reference 32

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This paper cites A deeper look into aleatoric and epistemic uncertainty disentanglement, 2022.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression A deeper look into aleatoric and epistemic uncertainty disentanglement, 2022

Reference 33

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This paper cites Statistical Reasoning with Imprecise Probabilities, volume 42 of Monographs on Statistics and Applied Probability.

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Statistical Reasoning with Imprecise Probabilities, volume 42 of Monographs on Statistics and Applied Probability

Reference 34

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An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Unresolved cited work

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Pith citing papers

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Response Uncertainty and Probe Modeling: Two Sides of the Same Coin in LLM Interpretability? cites this paper.

Response Uncertainty and Probe Modeling: Two Sides of the Same Coin in LLM Interpretability? An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression

Reference 2025

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Aleatoric and Epistemic Uncertainty Measures for Ordinal Classification through Binary Reduction cites this paper.

Aleatoric and Epistemic Uncertainty Measures for Ordinal Classification through Binary Reduction An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression

Reference 10

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Uncertainty Quantification for Regression: A Unified Framework based on kernel scores cites this paper.

Uncertainty Quantification for Regression: A Unified Framework based on kernel scores An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression

Reference 10

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On the QUEST for Uncertainty Quantification via Highest Density Regions cites this paper.

On the QUEST for Uncertainty Quantification via Highest Density Regions An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression

Reference 6

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Observation 42162445-8a3e-4337-a381-3af926d381ac · inbound

Subjective Risk Decomposition: A New View for Uncertainty Quantification cites this paper.

Subjective Risk Decomposition: A New View for Uncertainty Quantification An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression

Reference 2024

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Mobile Network Control with a World Model cites this paper.

Mobile Network Control with a World Model An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression

Reference 25

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