Paper Citation Record · LEDGER

On Reconstructing Training Data From Bayesian Posteriors and Trained Models

As of 20 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2507.18372.

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

pith.paper-citation-record.v1
2507.18372 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:22:21.596838Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

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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

34 of 34 outbound references displayed

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External citation measurements

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Outbound references

Observation 33855ecf-4da4-45a6-ae1b-7a264501eecc · outbound

This paper cites Differentially private simple linear regression.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Differentially private simple linear regression

Reference 1

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Observation 353b8a51-0e06-4084-902f-f4ccc1de80c6 · outbound

This paper cites Reconstructing training data with informed adversaries.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Reconstructing training data with informed adversaries

Reference 2

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Observation 8bca615c-f0bf-4f31-88fe-1082e79e7f6b · outbound

This paper cites an unresolved cited work.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Unresolved cited work

Reference 3

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Observation 40eecf5e-6ed0-4a26-8ddd-eacc37dc0ff4 · outbound

This paper cites and Sheldon, D.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models and Sheldon, D

Reference 4

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This paper cites and Ramamoorthi, R.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models and Ramamoorthi, R

Reference 5

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Observation 117a6642-c722-4af4-a06e-fc2ab04b43e4 · outbound

This paper cites Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses

Reference 6

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This paper cites and Berger, R.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models and Berger, R

Reference 7

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Observation 552c35f7-1275-46ec-a187-188bdbda0c04 · outbound

This paper cites and Steinwart, I.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models and Steinwart, I

Reference 8

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This paper cites Exposed! a survey of attacks on private data.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Exposed! a survey of attacks on private data

Reference 9

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Observation 5711b260-b454-45a4-aedb-b50ebfa44dd6 · outbound

This paper cites and Hill, J.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models and Hill, J

Reference 10

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Observation 06af37e7-7ed1-4083-aa4b-68fce4c8773a · outbound

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On Reconstructing Training Data From Bayesian Posteriors and Trained Models Unresolved cited work

Reference 11

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Observation 0a8c9bfe-972f-4d1f-9591-05b14fc7bc70 · outbound

This paper cites M., Rasch, M.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models M., Rasch, M

Reference 12

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This paper cites Bounding training data reconstruction in private (deep) learning.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Bounding training data reconstruction in private (deep) learning

Reference 13

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Observation 6420bd97-5660-4c06-975e-4a1a221d9891 · outbound

This paper cites Reconstructing training data from trained neural networks.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Reconstructing training data from trained neural networks

Reference 14

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Observation 7a39b92c-691e-43f9-bf74-2648d68103a3 · outbound

This paper cites Bounding Training Data Reconstruction in DP-SGD.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Bounding Training Data Reconstruction in DP-SGD

Reference 15

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Observation 0d137f1f-3d8d-42b9-b910-b67fa8d52e81 · outbound

This paper cites Deep residual learning for image recognition.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Deep residual learning for image recognition

Reference 16

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Observation b169b1a1-1136-47a6-beea-afdb5b3deffe · outbound

This paper cites Estimation of non-normalized statistical models by score matching.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Estimation of non-normalized statistical models by score matching

Reference 17

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Observation cebbb96b-f3bf-4011-a865-66a515f45cd8 · outbound

This paper cites Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy

Reference 18

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Observation 892a0039-8b30-4186-b792-de790f370563 · outbound

This paper cites Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation

Reference 19

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This paper cites posteriordb: a set of posteriors for Bayesian inference and probabilistic programming , October 2023.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models posteriordb: a set of posteriors for Bayesian inference and probabilistic programming , October 2023

Reference 20

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Observation c4778b2f-5f47-4428-b473-14ed6ebd9096 · outbound

This paper cites Bayesian pseudocoresets.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Bayesian pseudocoresets

Reference 21

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Observation 65fd2c98-4ebf-42fc-b69e-f8ec1d5a530c · outbound

This paper cites Kernel mean embedding of distributions: A review and beyond.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Kernel mean embedding of distributions: A review and beyond

Reference 22

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Observation cf0c2389-424c-4311-9fdc-79797e5222fd · outbound

This paper cites How to dp-fy ml: A practical tutorial to machine learning with differential privacy.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models How to dp-fy ml: A practical tutorial to machine learning with differential privacy

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:22:21.822120Z

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Observation 778326b6-fb01-4d2c-93a9-2290fe37c743 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models High-resolution image synthesis with latent diffusion models

Reference 24

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This paper cites V., Geiping, J., Schönlieb, C.-B., and Moeller, M.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models V., Geiping, J., Schönlieb, C.-B., and Moeller, M

Reference 25

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This paper cites and McAuley, J.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models and McAuley, J

Reference 26

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Observation f1f2c2ca-2ab7-405c-bcd9-6fb04c1b3e69 · outbound

This paper cites Membership inference attacks against machine learning models.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Membership inference attacks against machine learning models

Reference 27

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On Reconstructing Training Data From Bayesian Posteriors and Trained Models and Ermon, S

Reference 28

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Observation e6d1ee05-b0ac-49c8-a767-3c45c5092b3a · outbound

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

On Reconstructing Training Data From Bayesian Posteriors and Trained Models Sliced score matching: A scalable approach to density and score estimation

Reference 29

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This paper cites K., Fukumizu, K., and Lanckriet, G.

On Reconstructing Training Data From Bayesian Posteriors and Trained Models K., Fukumizu, K., and Lanckriet, G

Reference 30

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On Reconstructing Training Data From Bayesian Posteriors and Trained Models An explicit description of the reproducing kernel hilbert spaces of gaussian rbf kernels

Reference 31

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On Reconstructing Training Data From Bayesian Posteriors and Trained Models Machine Learning and the Future of Bayesian Computation

Reference 32

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On Reconstructing Training Data From Bayesian Posteriors and Trained Models Bayes hilbert spaces for posterior approximation

Reference 33

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On Reconstructing Training Data From Bayesian Posteriors and Trained Models Deep leakage from gradients

Reference 34

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Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.