Paper Citation Record · LEDGER

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2606.05073.

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

pith.paper-citation-record.v1
2606.05073 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T07:14:58.005004Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

36 of 36 outbound references displayed

verified exact3
verified fuzzy0
unresolved33
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fa815445-8c31-4221-8c5f-d6be948ff9f4 · outbound

This paper cites A gentle introduction to imputation of missing values.Journal of clinical epidemiology, 59(10):1087–1091, 2006.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness A gentle introduction to imputation of missing values.Journal of clinical epidemiology, 59(10):1087–1091, 2006

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 4f639128-516d-4e5e-9c40-30b092bef7e5 · outbound

This paper cites A survey on missing data in machine learning.Journal of Big data, 8(1):140, 2021.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness A survey on missing data in machine learning.Journal of Big data, 8(1):140, 2021

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 85e565ff-e74f-432b-8669-53d91e5a57c4 · outbound

This paper cites Dempster, Nan M.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Dempster, Nan M

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation f4626bcf-43d6-45e1-a6ce-65479ff73685 · outbound

This paper cites An overview of multiple imputation.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness An overview of multiple imputation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 2da88891-2073-4cd2-a10c-da149505242d · outbound

This paper cites mice: Multivariate imputation by chained equations in r.Journal of Statistical Software, 45(3):1–67, 2011.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness mice: Multivariate imputation by chained equations in r.Journal of Statistical Software, 45(3):1–67, 2011

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 11ede484-7d1e-4e91-8f28-27d37855e635 · outbound

This paper cites Stekhoven and Peter Bühlmann.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Stekhoven and Peter Bühlmann

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 1712a8dd-f5d1-49b4-be95-9a83f2f439e4 · outbound

This paper cites Gain: Missing data imputation using generative adversarial nets.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Gain: Missing data imputation using generative adversarial nets

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 39ef2c62-91dc-4804-a48d-70d3f0bb9afb · outbound

This paper cites MIWAE: Deep generative modelling and imputation of incomplete data sets.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness MIWAE: Deep generative modelling and imputation of incomplete data sets

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation e8aef5ac-dc6b-40b8-939d-5d335de00866 · outbound

This paper cites Csdi: Conditional score-based diffusion models for probabilistic time series imputation.Advances in neural information processing systems, 34:24804–24816, 2021.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Csdi: Conditional score-based diffusion models for probabilistic time series imputation.Advances in neural information processing systems, 34:24804–24816, 2021

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 6db75a4f-d598-4097-81bc-6fd58ef31b41 · outbound

This paper cites Diffusion models for missing value imputation in tabular data.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Diffusion models for missing value imputation in tabular data

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation a6b8f073-9d10-40d0-bf87-fcaa16560845 · outbound

This paper cites Inference and missing data.Biometrika, 63(3):581–592, 1976.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Inference and missing data.Biometrika, 63(3):581–592, 1976

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation aa4e0887-1378-45c5-bfa0-d7879c503183 · outbound

This paper cites Learning to Diagnose with LSTM Recurrent Neural Networks.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Learning to Diagnose with LSTM Recurrent Neural Networks

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-02T06:56:44.801250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3521df4b-5d8a-4418-b596-8783f8ff4f2b · outbound

This paper cites Missing data.The SAGE handbook of quantitative methods in psychology, 23:72–89, 2009.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Missing data.The SAGE handbook of quantitative methods in psychology, 23:72–89, 2009

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 869aac82-f187-4304-a930-28c081342b47 · outbound

This paper cites Collaborative filtering for implicit feedback datasets.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Collaborative filtering for implicit feedback datasets

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation bffabb4e-45df-4550-851d-7be2d972fcfc · outbound

This paper cites BPR: Bayesian Personalized Ranking from Implicit Feedback.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness BPR: Bayesian Personalized Ranking from Implicit Feedback

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-07-02T06:56:44.804725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 038bc900-17e1-4269-b4ce-5a7d54c12f91 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 7b480ea6-7f5d-46e7-9252-1ffebe71e47a · outbound

This paper cites Score-based generative modeling through stochastic differential equations.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Score-based generative modeling through stochastic differential equations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 878ea135-a3dd-4a5f-bdba-61c84f8fbd84 · outbound

This paper cites an unresolved cited work.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 1c589348-3e33-4aff-921a-46f3e0c01dea · outbound

This paper cites John Wiley & Sons, 2019.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness John Wiley & Sons, 2019

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation b5b23d99-8f3b-4137-8883-3aca52237cb4 · outbound

This paper cites HyperImpute: Generalized iterative imputation with automatic model selection.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness HyperImpute: Generalized iterative imputation with automatic model selection

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation ed0b3cf3-f30c-43b9-b382-d6b101a5b684 · outbound

This paper cites Remasker: Imputing tabular data with masked autoencoding.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Remasker: Imputing tabular data with masked autoencoding

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 0bf54e7f-be83-4c19-a9ca-41f1583c5cb1 · outbound

This paper cites CACTI: Leveraging copy masking and contextual information to improve tabular data imputation.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness CACTI: Leveraging copy masking and contextual information to improve tabular data imputation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 8c190e56-b88a-49e6-bef7-e27db00829a5 · outbound

This paper cites MissDiff: Training Diffusion Models on Tabular Data with Missing Values.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness MissDiff: Training Diffusion Models on Tabular Data with Missing Values

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-07-02T06:56:44.798453Z

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Observation 1e7bf9b4-cf87-492f-801c-e3679b6eb2cc · outbound

This paper cites an unresolved cited work.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 0fea2bfc-b895-42db-bfec-1c7a01d8421c · outbound

This paper cites Generating and imputing tabular data via diffusion and flow-based gradient-boosted trees.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Generating and imputing tabular data via diffusion and flow-based gradient-boosted trees

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation e310afb0-7b53-4b46-ae92-3daf419cea79 · outbound

This paper cites García-Laencina, José-Luis Sancho-Gómez, and Aníbal R.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness García-Laencina, José-Luis Sancho-Gómez, and Aníbal R

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 1b93ddc6-1a28-4a02-8ed5-cb73a546def6 · outbound

This paper cites Recurrent neural networks for multivariate time series with missing values.Scientific reports, 8(1):6085, 2018.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Recurrent neural networks for multivariate time series with missing values.Scientific reports, 8(1):6085, 2018

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 013dbbe5-fd09-4080-a60f-e8bba68b8c75 · outbound

This paper cites Preserving missing data distribution in synthetic data.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Preserving missing data distribution in synthetic data

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 74156529-afb9-47f7-8b46-76ce5d74d6e7 · outbound

This paper cites Estimation of non-normalized statistical models by score matching.Journal of Machine Learning Research, 6(4), 2005.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Estimation of non-normalized statistical models by score matching.Journal of Machine Learning Research, 6(4), 2005

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation bf05b581-ec8a-48d8-8565-aa611d9b2f0d · outbound

This paper cites A connection between score matching and denoising autoencoders.Neural computation, 23(7):1661–1674, 2011.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness A connection between score matching and denoising autoencoders.Neural computation, 23(7):1661–1674, 2011

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation cd585430-d587-4469-a38a-5a6b083da9d5 · outbound

This paper cites Sliced score matching: A scalable approach to density and score estimation.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Sliced score matching: A scalable approach to density and score estimation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Observation 7bd2c6a0-981f-4a37-bc8c-495b13bfc644 · outbound

This paper cites Mimic-iv-ed.PhysioNet, 2021.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Mimic-iv-ed.PhysioNet, 2021

Reference 32

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Observation 00bec5e5-8664-4b88-a833-271817b9c9ff · outbound

This paper cites Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565– 26577, 2022.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565– 26577, 2022

Reference 33

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Observation 81a35ec1-9bc7-47f8-abac-6f7dac1d4992 · outbound

This paper cites TabDDPM: Modelling tabular data with diffusion models.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness TabDDPM: Modelling tabular data with diffusion models

Reference 34

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Observation 1e6e5f5c-89cb-4030-b4d5-9794c87adf81 · outbound

This paper cites Schork, Kenneth Kendler, Päivi Pajukanta, Jonathan Flint, Noah Zaitlen, Na Cai, Andy Dahl, and Sriram Sankararaman.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness Schork, Kenneth Kendler, Päivi Pajukanta, Jonathan Flint, Noah Zaitlen, Na Cai, Andy Dahl, and Sriram Sankararaman

Reference 35

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Observation 9ae2c80b-df04-4236-8533-f74ae8cf2b6e · outbound

This paper cites MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-28T07:14:58.005004Z

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Pith citing papers

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