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Paper Citation Record · LEDGER

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff

As of 21 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2509.04363.

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

pith.paper-citation-record.v1
2509.04363 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:19:24.578953Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 95f74cf3-4ed1-4b5f-a900-3b44047f69ca · outbound

This paper cites A survey of deep active learning.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff A survey of deep active learning

Reference 1

Resolution
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Source-reported events for the cited work

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Observation d3723235-473e-448f-8243-7d5ed7547fa5 · outbound

This paper cites Active learning literature survey.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Active learning literature survey

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3e21f86a-a06e-45f1-ad46-81adafe5e88c · outbound

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

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Aleatory or epistemic? does it matter? Structural Safety, 31(2):105–112, 2009

Reference 3

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Source-reported events for the cited work

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Observation 46176807-d3ea-4841-a52a-9539b010eb76 · outbound

This paper cites The future of machine learning within target identification: Causality, reversibility, and druggability.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff The future of machine learning within target identification: Causality, reversibility, and druggability

Reference 4

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation fad4401a-de2e-4854-91d6-bf963ac513d6 · outbound

This paper cites Recover identifies synergistic drug combinations in vitro through sequential model optimization.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Recover identifies synergistic drug combinations in vitro through sequential model optimization

Reference 5

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 70d15ae2-2cd9-4457-b584-332d1f1a81a4 · outbound

This paper cites No Foundations without Foundations -- Why semi-mechanistic models are essential for regulatory biology.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff No Foundations without Foundations -- Why semi-mechanistic models are essential for regulatory biology

Reference 6

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Source-reported events for the cited work

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Observation 5891b197-f095-478f-a0a7-bac6bfc915a3 · outbound

This paper cites scperturb: harmonized single-cell perturbation data.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff scperturb: harmonized single-cell perturbation data

Reference 7

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1e63fe57-cd55-40d9-ac05-96aa745ebfad · outbound

This paper cites sctrends: A living review of commercial single-cell and spatial’omic technologies.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff sctrends: A living review of commercial single-cell and spatial’omic technologies

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 6591d2f6-4845-4720-82af-c2af32e738f1 · outbound

This paper cites A community effort to track commercial single-cell and spatial’omic technologies and business trends.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff A community effort to track commercial single-cell and spatial’omic technologies and business trends

Reference 9

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation d2cfee8a-cafe-4a91-87c3-c990d452848d · outbound

This paper cites Sequential exploration of unknown multi-dimensional functions as an aid to optimization.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Sequential exploration of unknown multi-dimensional functions as an aid to optimization

Reference 10

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Source-reported events for the cited work

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Observation 9150f007-173a-4ba8-954d-ee66db44da5b · outbound

This paper cites Jones, Matthias Schonlau, and William J.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Jones, Matthias Schonlau, and William J

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e940a40f-aa83-4251-9ded-77febf07a6fa · outbound

This paper cites On a measure of the information provided by an experiment.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff On a measure of the information provided by an experiment

Reference 12

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation eaaf0957-53d6-4977-8f20-9fcb4c4da218 · outbound

This paper cites Query by committee.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Query by committee

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation ba60c3e2-306d-4cd0-b4e5-d7152774f7e9 · outbound

This paper cites PyRelationAL: a python library for active learning research and development.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff PyRelationAL: a python library for active learning research and development

Reference 14

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Source-reported events for the cited work

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Observation f618d6a8-05d2-45e0-96a3-79df5056cddf · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision? In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff What uncertainties do we need in bayesian deep learning for computer vision? In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M

Reference 15

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 5cd87a52-15ec-4898-b374-bb8bbba0e828 · outbound

This paper cites Understanding measures of uncertainty for adversarial example detection.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Understanding measures of uncertainty for adversarial example detection

Reference 16

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 858c3ed8-bab6-43c0-865c-56be589080b4 · outbound

This paper cites Unifying approaches in active learning and active sampling via fisher information and information-theoretic quantities.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Unifying approaches in active learning and active sampling via fisher information and information-theoretic quantities

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 21048279-f67d-4c5a-b468-c1aaae34cb69 · outbound

This paper cites A generalized bias-variance decomposition for bregman divergences.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff A generalized bias-variance decomposition for bregman divergences

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 27f86791-c167-40a3-b4cd-3b27eb92c9be · outbound

This paper cites Understanding the bias-variance tradeoff of Bregman divergences.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Understanding the bias-variance tradeoff of Bregman divergences

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d5c16608-47ba-47b5-b2d2-6f7bbdc65215 · outbound

This paper cites Bayesian active learning for classification and preference learning, 2011.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Bayesian active learning for classification and preference learning, 2011

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 88befa7d-aaef-4550-99bc-fe6bd7694195 · outbound

This paper cites Speedup matrix completion with side information: Application to multi-label learning.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Speedup matrix completion with side information: Application to multi-label learning

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation b1791a6f-842c-4e11-b5b3-fdc6033196e6 · outbound

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When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Adam: A Method for Stochastic Optimization

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a32ebdc2-ed90-46e0-9193-aa127b789ea4 · outbound

This paper cites Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition

Reference 23

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verified exact
local_arxiv, observed 2026-08-05T10:19:24.723772Z

Source-reported events for the cited work

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Observation 7e28eb20-aa7b-4bff-944f-80289601a606 · outbound

This paper cites Rethinking bias-variance trade-off for generalization of neural networks.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Rethinking bias-variance trade-off for generalization of neural networks

Reference 24

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation a7197fc0-c5d0-4419-b3c2-3d5df24e20b1 · outbound

This paper cites Bias-variance decompositions: the exclusive privilege of bregman divergences.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Bias-variance decompositions: the exclusive privilege of bregman divergences

Reference 25

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 505fbe9e-c023-4c9a-9073-a7465d61baab · outbound

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When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Unresolved cited work

Reference 26

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unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 249a3407-5409-4aaf-85b9-7d676bc52c92 · outbound

This paper cites an unresolved cited work.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff Unresolved cited work

Reference 27

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unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 9c7f8464-1ac4-4e50-b556-2e19744566c1 · outbound

This paper cites add zero.

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff add zero

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-05T10:19:24.787217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Pith citing papers

No inbound Pith citation observations are available.