On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning | Paper Citation Record

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This paper cites Wiley Publications in Statistics, 1954.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Wiley Publications in Statistics, 1954

Reference 1

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Observation 403da893-58f6-443f-9f27-d251f4a8c066 · outbound

This paper cites John Wiley & Sons, 2009.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning John Wiley & Sons, 2009

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:10.815169Z

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Observation 6f1f7ba9-a846-4e91-9265-0f003689494d · outbound

This paper cites Strictly proper scoring rules, prediction, and estimation.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Strictly proper scoring rules, prediction, and estimation

Reference 3

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This paper cites Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi

Reference 4

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This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 5

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This paper cites Weight Uncertainty in Neural Networks.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Weight Uncertainty in Neural Networks

Reference 6

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This paper cites Springer, 2005.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Springer, 2005

Reference 7

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This paper cites The frontier of simulation-based inference.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning The frontier of simulation-based inference

Reference 8

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This paper cites Science and statistics.Journal of the American Statistical Association, 71 (356):791–799, 1976.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Science and statistics.Journal of the American Statistical Association, 71 (356):791–799, 1976

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:10.630121Z

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Observation 2830c1f8-1c37-4a25-bbe6-8820ca13f4d8 · outbound

This paper cites Oberkampf and Christopher J.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Oberkampf and Christopher J

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:10.458307Z

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Observation a786edf7-607d-4893-83b4-ae12c1b2f7d8 · outbound

This paper cites Cambridge university press, 2014.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Cambridge university press, 2014

Reference 11

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Observation e7c77ef0-1b5e-4c64-9273-97c6b5b4af44 · outbound

This paper cites Statistical inference.Australia: Duxbury/Thomson Learning, 2002.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Statistical inference.Australia: Duxbury/Thomson Learning, 2002

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:10.306682Z

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Observation a0d45284-eab8-44c4-9583-f2924846c520 · outbound

This paper cites Bayesian data analysis, 3rd edn london, 2013.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Bayesian data analysis, 3rd edn london, 2013

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:10.135297Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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This paper cites Conformalized quantile regression.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Conformalized quantile regression

Reference 14

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This paper cites The limits of distribution-free conditional predictive inference.Information and Inference: A Journal of the IMA, 10(2):455–482, 2021.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning The limits of distribution-free conditional predictive inference.Information and Inference: A Journal of the IMA, 10(2):455–482, 2021

Reference 15

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This paper cites Indirect inference.Journal of applied econometrics, 8(S1):S85–S118, 1993.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Indirect inference.Journal of applied econometrics, 8(S1):S85–S118, 1993

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:09.961135Z

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Observation fd34c422-17a9-4402-ba65-c8835e11c835 · outbound

This paper cites Springer, 1977.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Springer, 1977

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:09.804818Z

Source-reported events for the cited work

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Observation 854ee82d-f479-4710-81e9-3789837ab595 · outbound

This paper cites Springer, 2005.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Springer, 2005

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:09.687398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e07b9a2e-fb96-4c42-a11a-7e5e5a42aa04 · outbound

This paper cites Fastϵ -free inference of simulation models with bayesian conditional density estimation.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Fastϵ -free inference of simulation models with bayesian conditional density estimation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:09.525707Z

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Observation eda58bab-f948-4a21-98fd-88abaebedfa6 · outbound

This paper cites Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:09.392338Z

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This paper cites Likelihood-free MCMC with Amortized Approximate Ratio Estimators.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 21

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This paper cites Transmission of Justification and Warrant.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Transmission of Justification and Warrant

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:09.164985Z

Source-reported events for the cited work

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This paper cites Cambridge University Press, 2006.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Cambridge University Press, 2006

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:08.861713Z

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This paper cites Outline of a theory of statistical estimation based on the classical theory of probability.Philosophical Transactions of the Royal Society of London.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Outline of a theory of statistical estimation based on the classical theory of probability.Philosophical Transactions of the Royal Society of London

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:08.614397Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 590460ff-9673-4765-9207-15c592139858 · outbound

This paper cites University of Chicago Press, 1996.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning University of Chicago Press, 1996

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:08.371887Z

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This paper cites an unresolved cited work.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:15:08.116867Z

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Observation 8d220400-6bfb-4dcf-9a84-6ebe5d1207c2 · outbound

This paper cites John Wiley & Sons, 2017.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning John Wiley & Sons, 2017

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:07.906273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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This paper cites OUP Oxford, 2004.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning OUP Oxford, 2004

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:07.644436Z

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This paper cites On fiducial inference.The Annals of Mathematical Statistics, 32(3):661–676, 1961.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning On fiducial inference.The Annals of Mathematical Statistics, 32(3):661–676, 1961

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:07.417976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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This paper cites Fiducial inference, then and now.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Fiducial inference, then and now

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:07.190355Z

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This paper cites Generalized fiducial inference: A review and new results.Journal of the American Statistical Association, 111(515):1346–1361, 2016.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Generalized fiducial inference: A review and new results.Journal of the American Statistical Association, 111(515):1346–1361, 2016

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:06.963219Z

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This paper cites Courier Corporation, 2013.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Courier Corporation, 2013

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:06.747219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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This paper cites Citeseer, 1962.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Citeseer, 1962

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:06.477710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ee37418f-cb2c-46ce-a094-793c1d4175e0 · outbound

This paper cites Cambridge university press, 2003.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Cambridge university press, 2003

Reference 35

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Source-reported events for the cited work

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This paper cites Verification, validation, and predictive capability in computational engineering and physics.Appl.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Verification, validation, and predictive capability in computational engineering and physics.Appl

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:06.255143Z

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Observation 001f2bc9-8323-401d-ba57-1e69419b3e2e · outbound

This paper cites Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods.Machine learning, 110(3):457–506, 2021.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods.Machine learning, 110(3):457–506, 2021

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:06.008499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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This paper cites Aleatory or epistemic? does it matter?Structural safety, 31(2):105–112, 2009.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Aleatory or epistemic? does it matter?Structural safety, 31(2):105–112, 2009

Reference 38

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Source-reported events for the cited work

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This paper cites Explainable uncertainty quantifications for deep learning-based molecular property prediction.Journal of Cheminformatics, 15(1):13, 2023.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Explainable uncertainty quantifications for deep learning-based molecular property prediction.Journal of Cheminformatics, 15(1):13, 2023

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:05.775985Z

Source-reported events for the cited work

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Observation 51acd74e-2921-4ad2-8799-eac5052b026b · outbound

This paper cites Bayesian astrostatistics: a backward look to the future.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Bayesian astrostatistics: a backward look to the future

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:05.551977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 555c7ba0-8455-48fb-97fb-f800c305cf5c · outbound

This paper cites Towards reliable simulation-based inference with balanced neural ratio estimation.Advances in Neural Information Processing Systems, 35:20025–20037, 2022.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Towards reliable simulation-based inference with balanced neural ratio estimation.Advances in Neural Information Processing Systems, 35:20025–20037, 2022

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:05.326693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a1b3fea-2807-4524-b1ac-744f1dd9684c · outbound

This paper cites Bayes and frequentism: a particle physicist’s perspective.Contemporary Physics, 54(1):1–16, February 2013.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Bayes and frequentism: a particle physicist’s perspective.Contemporary Physics, 54(1):1–16, February 2013

Reference 42

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Source-reported events for the cited work

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Observation 5128d6ec-a455-4cbe-854e-d5988d251592 · outbound

This paper cites Conformal prediction with temporal quantile adjustments.Advances in Neural Information Processing Systems, 35:31017–31030, 2022.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Conformal prediction with temporal quantile adjustments.Advances in Neural Information Processing Systems, 35:31017–31030, 2022

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T11:15:03.552831Z

Source-reported events for the cited work

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Observation 438da0dc-273e-4be7-8e1e-dcc639b9499b · outbound

This paper cites Conformal Prediction Intervals with Temporal Dependence.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Conformal Prediction Intervals with Temporal Dependence

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:15:04.613916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3ee05fbe-f898-492f-9bd1-b39011e24d2b · outbound

This paper cites Validating Bayesian Inference Algorithms with Simulation-Based Calibration.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Validating Bayesian Inference Algorithms with Simulation-Based Calibration

Reference 45

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Source-reported events for the cited work

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Observation 8b3e5a6c-5504-401e-8572-266b7e7682f8 · outbound

This paper cites Bayesianly justifiable and relevant frequency calculations for the applied statistician.The Annals of Statistics, pages 1151–1172, 1984.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Bayesianly justifiable and relevant frequency calculations for the applied statistician.The Annals of Statistics, pages 1151–1172, 1984

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:15:05.074266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 95c0abc5-4b5a-4a2f-9928-2f82d4f33a82 · outbound

This paper cites Posterior predictive assessment of model fitness via realized discrepancies.Statistica sinica, pages 733–760, 1996.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Posterior predictive assessment of model fitness via realized discrepancies.Statistica sinica, pages 733–760, 1996

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T11:15:03.858685Z

Source-reported events for the cited work

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Observation f5f12203-d0c8-4351-9f15-055adee6ceb7 · outbound

This paper cites Predicting good probabilities with supervised learning.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Predicting good probabilities with supervised learning

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T11:15:03.916791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7ae2318-3340-4fbf-85a1-53766d2ad979 · outbound

This paper cites The comparison and evaluation of forecasters.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning The comparison and evaluation of forecasters

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T11:15:03.987047Z

Source-reported events for the cited work

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Observation 7d96905c-00f3-4ed1-a1f6-a310cb313407 · outbound

This paper cites Conformal Prediction With Conditional Guarantees.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Conformal Prediction With Conditional Guarantees

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T11:15:04.067607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65713567-3e0c-46cf-8ccc-fb42d4f2cdda · outbound

This paper cites Uncertainty cards,.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Uncertainty cards,

Reference 51

Resolution: verified exact; doi, observed 2026-08-07T11:15:04.388570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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