Paper Citation Record · LEDGER

Improving Noise Efficiency in Privacy-preserving Dataset Distillation

As of 17 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2508.01749.

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

pith.paper-citation-record.v1
2508.01749 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:32:48.192090Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

40 of 40 outbound references displayed

verified exact0
verified fuzzy31
unresolved8
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 25b5e0a5-aa96-4179-82d9-357d11af5e55 · outbound

This paper cites Deep learning with differential privacy.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Deep learning with differential privacy

Reference 1

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Source-reported events for the cited work

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Observation 7f88d7c9-768c-4d26-8ce3-067a4183b78d · outbound

This paper cites Don’t generate me: Training differen- tially private generative models with sinkhorn divergence.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Don’t generate me: Training differen- tially private generative models with sinkhorn divergence

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:53.705654Z

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Observation 3710a052-ed5f-43dd-ba71-6976f11d8aaa · outbound

This paper cites Cazenavette.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Cazenavette

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:53.696212Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad9f7891-095e-4ddc-a4da-1629882fc4e1 · outbound

This paper cites Private set generation with discriminative information.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Private set generation with discriminative information

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:53.686947Z

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Observation dcf3901b-c878-4e78-800c-2f67b878992a · outbound

This paper cites An analysis of single-layer networks in unsupervised feature learning.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation An analysis of single-layer networks in unsupervised feature learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:53.674403Z

Source-reported events for the cited work

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Observation 3d692967-eec0-4c2c-a4bc-f57b0aef8b09 · outbound

This paper cites an unresolved cited work.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-06T05:32:53.665204Z

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Observation 9fceffe9-8cdf-4ceb-b3e3-00f74903c34d · outbound

This paper cites The mnist database of handwritten digit images for machine learning research.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation The mnist database of handwritten digit images for machine learning research

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:53.656472Z

Source-reported events for the cited work

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Observation a58d24da-f09f-4b18-8032-569e6a98057e · outbound

This paper cites Differentially Private Diffusion Models.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Differentially Private Diffusion Models

Reference 8

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Observation 675bdc01-32e0-4ffd-b003-ccc0e3617b63 · outbound

This paper cites Minimizing the accumulated trajectory error to improve dataset distillation.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Minimizing the accumulated trajectory error to improve dataset distillation

Reference 9

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Observation d3fcd6f7-1265-42a3-915e-3f6cf01dce1c · outbound

This paper cites The algorithmic founda- tions of differential privacy.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation The algorithmic founda- tions of differential privacy

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:53.501740Z

Source-reported events for the cited work

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Observation a80cdf79-fa5a-4a70-874f-75b6fd987053 · outbound

This paper cites Differentially Private Diffusion Models Generate Useful Synthetic Images.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 11

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Source-reported events for the cited work

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Observation 52a60bc0-4889-4522-832c-af8361f6e2ed · outbound

This paper cites Dp-merf: Differentially private mean embeddings with ran- domfeatures for practical privacy-preserving data genera- tion.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Dp-merf: Differentially private mean embeddings with ran- domfeatures for practical privacy-preserving data genera- tion

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:53.358842Z

Source-reported events for the cited work

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Observation e0b2cf4c-b893-46f1-bb35-684a0dc2e4cf · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Adam: A Method for Stochastic Optimization

Reference 13

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Source-reported events for the cited work

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Observation 07d4277f-0bfb-4fb0-9db9-2c0b5dcb0ef1 · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Imagenet classification with deep convolutional neural net- works

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:53.076374Z

Source-reported events for the cited work

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Observation bbb18691-1a7c-4ed9-8b9f-96d25e9ddfa5 · outbound

This paper cites Dataset condensation with contrastive signals.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Dataset condensation with contrastive signals

Reference 15

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Source-reported events for the cited work

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Observation 158634ce-4b73-4f9c-b6ce-ccdb31d334f0 · outbound

This paper cites Dataset Distillation via the Wasserstein Metric.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Dataset Distillation via the Wasserstein Metric

Reference 16

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Source-reported events for the cited work

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Observation 04e0b437-6e0d-495d-a7d6-63473ae669fc · outbound

This paper cites Efficient dataset distillation using random feature approximation.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Efficient dataset distillation using random feature approximation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:52.700343Z

Source-reported events for the cited work

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Observation 19875fc5-9e91-4a91-81b6-460fd6004bf4 · outbound

This paper cites Dataset distillation with convexified implicit gra- dients.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Dataset distillation with convexified implicit gra- dients

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:52.463638Z

Source-reported events for the cited work

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Observation 102bb495-0851-4454-930f-b7ec19f08298 · outbound

This paper cites R ´enyi differential privacy.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation R ´enyi differential privacy

Reference 19

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Source-reported events for the cited work

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Observation 9fc40049-2f3b-42f5-bf89-559b69d28b67 · outbound

This paper cites Dataset distillation with infinitely wide convolutional networks.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Dataset distillation with infinitely wide convolutional networks

Reference 20

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Observation 981b2733-bbff-41f6-b189-d55d4c917ca7 · outbound

This paper cites Dataset meta-learning from kernel ridge-regression.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Dataset meta-learning from kernel ridge-regression

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:51.869066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9fd817b2-6aba-4b36-baf3-33244496ff32 · outbound

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

Improving Noise Efficiency in Privacy-preserving Dataset Distillation High-resolution image synthesis with latent diffusion models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:51.652373Z

Source-reported events for the cited work

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Observation 312fd1b5-9ec4-4f52-a2a7-7238f2be3369 · outbound

This paper cites Data distillation: A survey, 2023.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Data distillation: A survey, 2023

Reference 23

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Observation 523f5a36-7226-4976-8a0b-e71b24436d03 · outbound

This paper cites DataDAM: Efficient dataset distillation with attention matching.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation DataDAM: Efficient dataset distillation with attention matching

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:51.308844Z

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Observation 7390976d-70cb-466c-8d0d-4baa456a50c6 · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:51.121601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c07b1eb2-d6df-46b4-8b3c-bc4c898bcabd · outbound

This paper cites Loss-curvature matching for dataset selection and condensation.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Loss-curvature matching for dataset selection and condensation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:50.925386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cf6d57ea-2831-4368-af30-adb8a2d92c75 · outbound

This paper cites Denois- ing diffusion implicit models.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Denois- ing diffusion implicit models

Reference 27

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Source-reported events for the cited work

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Observation b1989359-e381-4079-a7d5-38ae38b9ae3b · outbound

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

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Score-based generative modeling through stochastic differential equa- tions

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:50.601270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 173bc111-08ba-4a79-bda7-d7d555a88e66 · outbound

This paper cites Differentially pri- vate kernel inducing points using features from scatternets (DP-KIP-scatternet) for privacy preserving data distillation.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Differentially pri- vate kernel inducing points using features from scatternets (DP-KIP-scatternet) for privacy preserving data distillation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:50.466085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dd06ca47-ceb0-4497-88b3-ba6de4cd5c21 · outbound

This paper cites Cafe: Learning to condense dataset by align- ing features.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Cafe: Learning to condense dataset by align- ing features

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:50.224031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 56406c11-9230-4f62-8c7d-b64918c63f00 · outbound

This paper cites Dataset Distillation.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Dataset Distillation

Reference 31

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Source-reported events for the cited work

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Observation 77208863-2694-488c-a1eb-50834132f6fa · outbound

This paper cites Subsampled r ´enyi differential privacy and analytical moments accountant.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Subsampled r ´enyi differential privacy and analytical moments accountant

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:50.030629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 57b7f485-5265-4dd3-9509-ab38db8a084a · outbound

This paper cites Fashion- mnist: a novel image dataset for benchmarking machine learning algorithms, 2017.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Fashion- mnist: a novel image dataset for benchmarking machine learning algorithms, 2017

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:49.814251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9521abe8-326d-46e4-ba6d-45531a6ee02c · outbound

This paper cites Opacus: User-Friendly Differential Privacy Library in PyTorch.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 34

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Source-reported events for the cited work

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Observation b5b9fa74-3caa-453e-bbf0-560b481762fb · outbound

This paper cites M3D: Dataset condensation by minimizing maximum mean discrepancy.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation M3D: Dataset condensation by minimizing maximum mean discrepancy

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:49.574545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d0a4d9c1-9050-40c8-98e7-b185c6fd683d · outbound

This paper cites Dataset condensation with dif- ferentiable siamese augmentation.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Dataset condensation with dif- ferentiable siamese augmentation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:49.368782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e9a6c0d7-29b3-44af-a9d3-5602fd441f0f · outbound

This paper cites Dataset condensation with gradient matching.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Dataset condensation with gradient matching

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:49.158157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ed573ff6-fdc1-4bc6-9a99-ee7dd3610b13 · outbound

This paper cites Improved distri- bution matching for dataset condensation.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Improved distri- bution matching for dataset condensation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:48.970942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3d64716d-6f3f-4949-b258-7bde6ac743da · outbound

This paper cites Differentially private dataset condensation.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Differentially private dataset condensation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:48.742592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7740c2e7-7915-4ed3-a3c6-d94ef96cb5c6 · outbound

This paper cites w/” and “w/o.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation w/” and “w/o

Reference 40

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T05:32:48.522825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

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