Paper Citation Record · LEDGER

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk

As of 11 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2507.08150.

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

pith.paper-citation-record.v1
2507.08150 v4

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:34:48.564548Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-16T07:32:23.756013Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T07:32:23.790928Z

Reference resolution

38 of 38 outbound references displayed

verified exact2
verified fuzzy19
unresolved16
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External citation measurements

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Outbound references

Observation 3c3cceea-6ac0-4872-a067-ae43e9524d12 · outbound

This paper cites an unresolved cited work.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:34:49.182090Z

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Observation eeb277d4-f725-4c8c-b441-00ed6621a78c · outbound

This paper cites However, our empirical results in Tables 14, 19 and 24 show that for reasonably sized datasets, this theoretical discrepancy does not visibly impact our practical marginal coverage.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk However, our empirical results in Tables 14, 19 and 24 show that for reasonably sized datasets, this theoretical discrepancy does not visibly impact our practical marginal coverage

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.174339Z

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Observation 168b4c4f-6725-4bb7-bfd7-8559fc02f4d5 · outbound

This paper cites If ⌈(1 − α)(|Dcal| + 1)⌉ > |Dcal|, set γ∗ 1 = ∞.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk If ⌈(1 − α)(|Dcal| + 1)⌉ > |Dcal|, set γ∗ 1 = ∞

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.150641Z

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Observation 1f2d3995-4d47-46ce-b4f1-3dbfebc0cf35 · outbound

This paper cites Lemma B.2.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Lemma B.2

Reference 4

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Observation 80403ed9-5352-4d18-b6c6-5ee94b101193 · outbound

This paper cites an unresolved cited work.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:34:49.205628Z

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Observation a6943fd8-b4fe-4d1b-9e01-9bc8ba969584 · outbound

This paper cites URL https://www.nature.com/articles/ s41586-024-08328-6.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk URL https://www.nature.com/articles/ s41586-024-08328-6

Reference 7

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Observation 149385e2-11a3-4652-a379-d585eb9c204e · outbound

This paper cites Conformal prediction intervals for the individual treatment effect.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Conformal prediction intervals for the individual treatment effect

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:48.494169Z

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Observation c3f67af1-a6af-4963-aeb2-4f62d71f2bf5 · outbound

This paper cites Quantifying Aleatoric and Epistemic Dynamics Uncertainty via Local Conformal Calibration.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Quantifying Aleatoric and Epistemic Dynamics Uncertainty via Local Conformal Calibration

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:48.497158Z

Source-reported events for the cited work

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Observation 14694edb-7ab5-4529-9dd8-2712a384b38f · outbound

This paper cites A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:48.502755Z

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Observation abd9a55e-e8ff-4eca-9526-4a60e0df7ac0 · outbound

This paper cites an unresolved cited work.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:48.505544Z

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Observation 625bec00-76da-4862-9af4-3cc55ce702d3 · outbound

This paper cites B EYOND PINBALL LOSS : QUANTILE METHODS FOR CALIBRATED UNCERTAINTY QUANTIFICATION.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk B EYOND PINBALL LOSS : QUANTILE METHODS FOR CALIBRATED UNCERTAINTY QUANTIFICATION

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.189946Z

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Observation b245d876-aa77-4788-ba23-05b0c4f97980 · outbound

This paper cites The optimal value λ∗ is found by optimizing the QuantileLoss on Dval, without using any data from Dcal (Step 4 and 5 of Algorithm 1).

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk The optimal value λ∗ is found by optimizing the QuantileLoss on Dval, without using any data from Dcal (Step 4 and 5 of Algorithm 1)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.166734Z

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Observation 681a8ed2-a4c0-4b5c-a371-50f3cb1587df · outbound

This paper cites an unresolved cited work.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:34:49.158196Z

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Observation d8e2e00b-8d2b-439d-984d-1437702741f6 · outbound

This paper cites If the calibration residuals are small by chance, conformal intervals may be too narrow, especially with tiny calibration datasets.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk If the calibration residuals are small by chance, conformal intervals may be too narrow, especially with tiny calibration datasets

Reference 22

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Observation da32b5e5-2ffb-4207-8e35-7243d77f65a5 · outbound

This paper cites This is crucial in human-in-the-loop settings, where interventions are prioritized based on an accurate ranking of predictive uncertainty across data points (see Appendix I).

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk This is crucial in human-in-the-loop settings, where interventions are prioritized based on an accurate ranking of predictive uncertainty across data points (see Appendix I)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.126971Z

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Observation 9b2042a0-4fa0-4f5f-8de4-023168ac10b4 · outbound

This paper cites Similarly, classical Random Forests are known to be consistent under standard assumptions (Scornet et al., 2015).

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Similarly, classical Random Forests are known to be consistent under standard assumptions (Scornet et al., 2015)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.117690Z

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Observation 86aa540b-6855-44de-a93e-9868cb916ada · outbound

This paper cites This suggests that XGBoost and its quantile version, QXGB, can be consistent for suitable choices of hyperparameters.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk This suggests that XGBoost and its quantile version, QXGB, can be consistent for suitable choices of hyperparameters

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.109699Z

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Observation 5a6376b9-9c39-415d-982f-844fe483edfd · outbound

This paper cites pinball loss.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk pinball loss

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.101693Z

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Observation 60b70e60-3e41-40dc-9af0-2e8e8d71c1ad · outbound

This paper cites an unresolved cited work.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:34:49.094176Z

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Observation 53d3d7ea-3108-4d89-8bf5-070ec9e36c8c · outbound

This paper cites In other words, Step 3 and 4 of Algorithm 1 together (approximately) solve (γ⋆ 1 , λ⋆) = arg min (γ1,λ)∈(0,∞)×Λ s.t.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk In other words, Step 3 and 4 of Algorithm 1 together (approximately) solve (γ⋆ 1 , λ⋆) = arg min (γ1,λ)∈(0,∞)×Λ s.t

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.086974Z

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Observation d69fece6-1c06-48ad-afa6-8e76e5353298 · outbound

This paper cites P[Y | X].

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk P[Y | X]

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.078629Z

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Observation c060317b-6b7a-48f4-9e4e-d92abf1b17fd · outbound

This paper cites an unresolved cited work.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:34:49.070821Z

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Observation e37ac8c9-8381-4548-a533-4cad3cbca941 · outbound

This paper cites E.g., λ0 = 0 for methods that do not explicitly model epistemic uncertainty (see Ap- pendix A.6), or λ0 = 1 for methods that model both but do not rebalance them (see Appendix A.2).

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk E.g., λ0 = 0 for methods that do not explicitly model epistemic uncertainty (see Ap- pendix A.6), or λ0 = 1 for methods that model both but do not rebalance them (see Appendix A.2)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.062967Z

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Observation e27baace-0dbe-46dd-a36f-f34c73af66dd · outbound

This paper cites It outputs Cγ1,0,γ2,base.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk It outputs Cγ1,0,γ2,base

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.055024Z

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Observation 8039f8d7-7cfc-4651-a100-e32b7ea52fe5 · outbound

This paper cites On the calibration set, absolute residuals ai = |yi − ˆf (xi)| are computed.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk On the calibration set, absolute residuals ai = |yi − ˆf (xi)| are computed

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.047688Z

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Observation d16f9084-c37c-48eb-86d9-d0ae8e5e1124 · outbound

This paper cites This implies γ1 = γ2.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk This implies γ1 = γ2

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.040042Z

Source-reported events for the cited work

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Observation 6ff5d728-aaee-4d12-9df8-e8059398f096 · outbound

This paper cites With γ1 = 1, then γ2 = λ and the prediction interval from Equation (3) reads as: Cγ1=1(x) = h ˆf (x) − ˆqale α/2(x) − λˆqepi α/2(x), ˆf (x) + ˆqale 1−α/2(x) + λˆqepi 1−α/2(x) i.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk With γ1 = 1, then γ2 = λ and the prediction interval from Equation (3) reads as: Cγ1=1(x) = h ˆf (x) − ˆqale α/2(x) − λˆqepi α/2(x), ˆf (x) + ˆqale 1−α/2(x) + λˆqepi 1−α/2(x) i

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.032616Z

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Observation b74b4fb1-ff36-4c3e-a922-0df9425a6cd1 · outbound

This paper cites relative vs. absolute uncertainty.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk relative vs. absolute uncertainty

Reference 36

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Observation 3ba588ea-4f27-40ef-88cb-6d024d610606 · outbound

This paper cites an unresolved cited work.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:34:49.024472Z

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Observation 04923d9c-3f27-4163-bf33-645cb8a58fce · outbound

This paper cites Even when conditional coverage is not required, improving relative uncertainty tends to reduce average interval width and improve marginal calibration under distribution shifts.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Even when conditional coverage is not required, improving relative uncertainty tends to reduce average interval width and improve marginal calibration under distribution shifts

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.016514Z

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Observation 2b0d8c99-df3c-4d56-9b0d-0391c60fea96 · outbound

This paper cites an unresolved cited work.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Unresolved cited work

Reference 1968

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Observation 459d14f0-ec22-4974-a1bd-cb3d05ed6681 · outbound

This paper cites an unresolved cited work.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Unresolved cited work

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:48.500075Z

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Observation 3bbad276-91ef-4bd5-aef2-a4e8ab44f46e · outbound

This paper cites What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:48.491470Z

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Observation 9f8e86c0-7d56-4258-b921-eaf0d0d4756a · outbound

This paper cites ambiguity in targets y for a given x.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk ambiguity in targets y for a given x

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:49.198334Z

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Observation 66820df5-9a34-4b2f-8ea2-791355a58698 · outbound

This paper cites an unresolved cited work.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Unresolved cited work

Reference 2020

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:34:49.213529Z

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Observation 555d5fff-ecef-4f4d-818b-8c1d2d31de63 · outbound

This paper cites an unresolved cited work.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Unresolved cited work

Reference 2021

Resolution: verified exact; doi, observed 2026-08-06T18:34:48.586855Z

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Observation ee84778e-bdfd-489b-ab97-78b3386d4e53 · outbound

This paper cites Theoretical Foundations of Conformal Prediction.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Theoretical Foundations of Conformal Prediction

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:48.476730Z

Source-reported events for the cited work

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Observation a4eb5e3e-3410-4aa0-b1ce-80ba0fd80726 · outbound

This paper cites an unresolved cited work.

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Unresolved cited work

Reference 2025

Resolution: verified exact; raw_fallback, observed 2026-08-06T18:34:49.007397Z

Source-reported events for the cited work

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Pith citing papers

Observation 374e3be3-fd8f-4cc1-9287-c04ab28c4c6f · inbound

Theoretical Foundations of Conformal Prediction cites this paper.

Theoretical Foundations of Conformal Prediction CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-07T03:18:08.792596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 13395141-9b5f-45a3-8b0c-9c715c3190fb · inbound

Calibrate, Don't Curate: Label-Efficient Estimation from Noisy LLM Judges cites this paper.

Calibrate, Don't Curate: Label-Efficient Estimation from Noisy LLM Judges CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-07T03:18:08.792596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.