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Paper Citation Record · LEDGER

Scaling Laws for Differentially Private Language Models

As of 10 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 3 inbound Pith citation observations for arXiv:2501.18914.

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

pith.paper-citation-record.v1
2501.18914 v1

Coverage vector

measured 83 of 83 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:05:02.705232Z

measured 86 of 86 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T02:50:09.196457Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T02:50:58.332378Z

Reference resolution

83 of 83 outbound references displayed

  • verified exact2
  • verified fuzzy46
  • unresolved34
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f0373ff8-9971-4279-a031-a6ba44d8f316 · outbound

This paper cites write newline.

Scaling Laws for Differentially Private Language Models write newline

Reference 1

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no resolver link, observed 2026-08-09T22:05:02.460455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.460455Z digest=sha256:2d80ee9c3b6c48ca9eba6697de84c290b9ff4882be641ba4335800dac18b16a3

Observation 61f594a2-8dd0-4d6c-bd0e-35f56ae47906 · outbound

This paper cites write newline.

Scaling Laws for Differentially Private Language Models write newline

Reference 2

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source=arxiv_source observed=2026-08-09T22:05:02.464676Z digest=sha256:e82d00fc153a0a7716ae30a2f96f8a929a7da9b0916a6137cb108f360bed39ba

Observation a02fce59-7c6a-4391-8ce3-37579efdf4a8 · outbound

This paper cites B., Mironov, I., Talwar, K., and Zhang, L.

Scaling Laws for Differentially Private Language Models B., Mironov, I., Talwar, K., and Zhang, L

Reference 3

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no resolver link, observed 2026-08-09T22:05:02.468415Z

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source=arxiv_source observed=2026-08-09T22:05:02.468415Z digest=sha256:d4040c58568c2e233c9c6b49222bc646acfcebd5032c83d5e3765e25b6ba4f95

Observation 875a1ea7-8e41-42da-89ba-f79f146975aa · outbound

This paper cites GPT-4 Technical Report.

Scaling Laws for Differentially Private Language Models GPT-4 Technical Report

Reference 4

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source=arxiv_source observed=2026-08-09T22:05:02.471763Z digest=sha256:e0c9a9bca5086e1ab50dc167ddff69e247657dd36486dad25725c1c4962e63bf

Observation a46ef737-e423-4636-84f9-8516cfe717dd · outbound

This paper cites The crossroads of innovation and privacy: Private synthetic data for generative AI.

Scaling Laws for Differentially Private Language Models The crossroads of innovation and privacy: Private synthetic data for generative AI

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.475509Z digest=sha256:ec2e0dfa63aab4d3c918f56a3f61e44953eeccf64d0354eda43dbc70ff313dcf

Observation d15e446e-47b6-442b-9e5e-0fbcaab2412c · outbound

This paper cites Private prediction for large-scale synthetic text generation.

Scaling Laws for Differentially Private Language Models Private prediction for large-scale synthetic text generation

Reference 6

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source=arxiv_source observed=2026-08-09T22:05:02.478985Z digest=sha256:be9bba91347afd10ff213cf18bb37ee3963e804baf9a2482ca8fc5884798e222

Observation 5ac5903d-fda1-4a9c-a007-0b513de417f4 · outbound

This paper cites Large-scale differentially private BERT.

Scaling Laws for Differentially Private Language Models Large-scale differentially private BERT

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.482745Z digest=sha256:9da14965bab66c8360f0ceac00244b5c15a9e50f37ed83a8456903ddf16a3861

Observation 7a2c9af1-4763-4bb4-88c1-68c25e77beba · outbound

This paper cites PaLM 2 Technical Report.

Scaling Laws for Differentially Private Language Models PaLM 2 Technical Report

Reference 8

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source=arxiv_source observed=2026-08-09T22:05:02.485922Z digest=sha256:53c082c9f8851540368d500a1a4442236f361041c656241ef29b38c6ff8eaf7f

Observation 4bdb8029-de59-4c2b-b0a2-d7f9d1169c81 · outbound

This paper cites Privacy amplification by subsampling: Tight analyses via couplings and divergences, 2018.

Scaling Laws for Differentially Private Language Models Privacy amplification by subsampling: Tight analyses via couplings and divergences, 2018

Reference 9

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raw_fallback, observed 2026-08-09T22:05:15.749559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.489773Z digest=sha256:a00cafa7bcfaa54ac8c0759aebb74390dd49abcd22388df50e5043998e3407f2

Observation c4ea427c-54a1-4fb6-8335-d8a221d33be2 · outbound

This paper cites Reconstructing training data with informed adversaries.

Scaling Laws for Differentially Private Language Models Reconstructing training data with informed adversaries

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.492996Z digest=sha256:ac7ca56dd9e5b8ca589e0e1e3fac791029d6f839e45e243892f35279eab8f3d1

Observation 5bab3390-66b1-4e27-90c5-f98b05735290 · outbound

This paper cites Private empirical risk minimization: Efficient algorithms and tight error bounds.

Scaling Laws for Differentially Private Language Models Private empirical risk minimization: Efficient algorithms and tight error bounds

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.495991Z digest=sha256:b4e25f9fc73f50245afd54db6ad154083d48ebd3e7d0a1d2ae1f06dc24637c83

Observation eea3ad11-cfac-4603-b784-4bf4cd836ef2 · outbound

This paper cites Unlocking Accuracy and Fairness in Differentially Private Image Classification.

Scaling Laws for Differentially Private Language Models Unlocking Accuracy and Fairness in Differentially Private Image Classification

Reference 12

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source=arxiv_source observed=2026-08-09T22:05:02.499144Z digest=sha256:28489a727ba56ec2f581cf6493069e84791402a5d858d057242d381c501cb3af

Observation e3d5a317-e77e-4395-ae95-ce8fff419810 · outbound

This paper cites S., Sutawika, L., Schoelkopf, H., Anthony, Q., Purohit, S., and Raff, E.

Scaling Laws for Differentially Private Language Models S., Sutawika, L., Schoelkopf, H., Anthony, Q., Purohit, S., and Raff, E

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.725626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.502400Z digest=sha256:4d320536b5b5813f1b8ee7f4e085a007a61e7d8df1d4875b27f1b8f1e2ee7d74

Observation 49a556cf-ce3c-42f5-a39f-07d14ef2ded5 · outbound

This paper cites Scalable and efficient training of large convolutional neural networks with differential privacy.

Scaling Laws for Differentially Private Language Models Scalable and efficient training of large convolutional neural networks with differential privacy

Reference 14

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raw_fallback, observed 2026-08-09T22:05:15.717505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.505391Z digest=sha256:7d8f75628ea8ada3092c63dfa63196223237ac3e7707a2865df89bf6fbeb30aa

Observation 3f005122-75f0-4d23-a70a-c9456393397c · outbound

This paper cites Differentially private optimization on large model at small cost.

Scaling Laws for Differentially Private Language Models Differentially private optimization on large model at small cost

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.709378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.508420Z digest=sha256:1512a9fc178f3edfeceffbb36a3095553330250e027893bc39d3c084bad1d55c

Observation 3deb14b3-6036-4044-abd7-fa063ceba6ec · outbound

This paper cites Extracting training data from large language models.

Scaling Laws for Differentially Private Language Models Extracting training data from large language models

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.701156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.511347Z digest=sha256:a1c8923153e2fa03fe47aabf288b4f134124f7feda44e73f42d857c1f36c2cad

Observation 895368f2-7799-4861-9444-ce22f1b1ffa0 · outbound

This paper cites Quantifying memorization across neural language models.

Scaling Laws for Differentially Private Language Models Quantifying memorization across neural language models

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.514388Z digest=sha256:263fca14c3e8ece6b16c99dc93751533c4440a90f05fdbeae5be5dc0a758494f

Observation 2a206b09-f01a-4bc1-8e81-19b919aa87bb · outbound

This paper cites A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., and Tram \`e r, F.

Scaling Laws for Differentially Private Language Models A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., and Tram \`e r, F

Reference 18

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raw_fallback, observed 2026-08-09T22:05:15.684835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.517425Z digest=sha256:e9d1180e9771be8db060f5a20c222dc801a23776e25349dad851ae3e57328bde

Observation f4006fd1-d80f-413e-a2dd-f0258cdd653a · outbound

This paper cites Fine-Tuning Large Language Models with User-Level Differential Privacy.

Scaling Laws for Differentially Private Language Models Fine-Tuning Large Language Models with User-Level Differential Privacy

Reference 19

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source=arxiv_source observed=2026-08-09T22:05:02.520377Z digest=sha256:709ffae1c1c52652684e45238f36724cd6052166488ffcb2df844bd814f652bb

Observation ca211374-f08d-44f9-80fb-7c41187d1ccf · outbound

This paper cites Symbolic Discovery of Optimization Algorithms.

Scaling Laws for Differentially Private Language Models Symbolic Discovery of Optimization Algorithms

Reference 20

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source=arxiv_source observed=2026-08-09T22:05:02.523732Z digest=sha256:1c5adaa689288177dd9f3470ff74d1fd2ebff195186eff741b73727dd939f8ff

Observation e21b2d25-e02d-4472-9a69-ad509a81ea54 · outbound

This paper cites an unresolved cited work.

Scaling Laws for Differentially Private Language Models Unresolved cited work

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.526910Z digest=sha256:3732b9892d03cfb20ab0b02bfdba830e2687efd69ce8d8ebbfb3574747f137ce

Observation dedc7793-05d4-437a-87d8-d0d3afbcad11 · outbound

This paper cites Mind the privacy unit! user-level differential privacy for language model fine-tuning.

Scaling Laws for Differentially Private Language Models Mind the privacy unit! user-level differential privacy for language model fine-tuning

Reference 22

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verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.668663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.529416Z digest=sha256:100bf8663b15c63323ac5dd4316f0b35f9ca07caca20916b186bcdde30ee559a

Observation b28f6f38-5cbb-4a31-8648-59dafbe8671b · outbound

This paper cites Scalable DP-SGD : Shuffling vs.

Scaling Laws for Differentially Private Language Models Scalable DP-SGD : Shuffling vs

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.531816Z digest=sha256:8aef9138b29de1fe28be48f970d8ea0385bbd7f2d5c9a08e031a518cb17dc2fe

Observation 304dea68-846e-4f8a-a2b4-55a2597a44d4 · outbound

This paper cites Unlocking High-Accuracy Differentially Private Image Classification through Scale.

Scaling Laws for Differentially Private Language Models Unlocking High-Accuracy Differentially Private Image Classification through Scale

Reference 24

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Observation 799558ed-aab7-495f-856f-0828e7cf1d72 · outbound

This paper cites BERT : Pre-training of deep bidirectional transformers for language understanding.

Scaling Laws for Differentially Private Language Models BERT : Pre-training of deep bidirectional transformers for language understanding

Reference 25

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raw_fallback, observed 2026-08-09T22:05:15.651406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.536687Z digest=sha256:fde4d7031b453f93e3ea74d24c1954d5cc05fe6dbc7281a49402e14e6e86ac4f

Observation dd298b68-236c-4c98-9fde-c471a95bdfa4 · outbound

This paper cites S., Wang, T., Huang, C., and Sun, H.

Scaling Laws for Differentially Private Language Models S., Wang, T., Huang, C., and Sun, H

Reference 26

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raw_fallback, observed 2026-08-09T22:05:15.642934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.539119Z digest=sha256:75fa9265f418e0678bbfbe5c1b101f8fcba8b3ac5b80fa3352ac478bcdff2013

Observation 247ce4aa-b7e9-4cbd-abe2-95d59c2926dd · outbound

This paper cites Flocks of stochastic parrots: Differentially private prompt learning for large language models.

Scaling Laws for Differentially Private Language Models Flocks of stochastic parrots: Differentially private prompt learning for large language models

Reference 27

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raw_fallback, observed 2026-08-09T22:05:15.635318Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.541494Z digest=sha256:588bb52e060630e6b32df00169338fd66f6e7fa9a665f01cd4f3ce34ff6c10c1

Observation 7b89da64-14c3-45b9-b4f5-e59a42c54203 · outbound

This paper cites On the privacy risk of in-context learning.

Scaling Laws for Differentially Private Language Models On the privacy risk of in-context learning

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.627467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.543817Z digest=sha256:e03c96433cdba31e17398adeb71699c9eeb525d00d20b5108d0be0e4c673ba11

Observation 0550f31a-a31d-4492-afb0-6c9bc3dd29d3 · outbound

This paper cites The Llama 3 Herd of Models.

Scaling Laws for Differentially Private Language Models The Llama 3 Herd of Models

Reference 29

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-09T22:05:02.546186Z digest=sha256:9719c1408f15f93fc2599bff8c354082fd62eae0e18beb39f41f5ea2def83948

Observation 78dda07b-586a-4f8a-9f13-8d2da79155ef · outbound

This paper cites Calibrating noise to sensitivity in private data analysis.

Scaling Laws for Differentially Private Language Models Calibrating noise to sensitivity in private data analysis

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.619209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.548692Z digest=sha256:a493dbce58c71b14363cece0fa10b937f49fe7e29a1dd342b846587e8d6cb211

Observation 822192f8-2cf8-41a8-a43b-7ef6ea8c6068 · outbound

This paper cites Language models scale reliably with over-training and on downstream tasks.

Scaling Laws for Differentially Private Language Models Language models scale reliably with over-training and on downstream tasks

Reference 31

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-09T22:05:02.550944Z digest=sha256:5fb73a61c0f6beb944949be62f926865db59d7eb299627376b7e854e4d48045e

Observation c493a56c-3e1d-4b3e-be08-4fb18e958e3d · outbound

This paper cites Predictability and surprise in large generative models.

Scaling Laws for Differentially Private Language Models Predictability and surprise in large generative models

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.610626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.554175Z digest=sha256:13f1d79a5a3bd37328b285e9aa4feefa016b1388b3934a217200fc7699d26319

Observation 5f30aae0-6cd9-4171-91ea-2497b41e13cc · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Scaling Laws for Differentially Private Language Models Gemini: A Family of Highly Capable Multimodal Models

Reference 33

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.557123Z digest=sha256:97e2dc7c48e1afd3d32df2c084a81b745c060ae2bf777ef3bed97ced3ae773a5

Observation 8cdadba0-1e12-4b4e-a025-6c47bd7578cc · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Scaling Laws for Differentially Private Language Models Gemma: Open Models Based on Gemini Research and Technology

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.560629Z digest=sha256:cf5001b7b4d499555cd2ba5d8c31acf62876c9b49f54861759ff7bda3bca87ee

Observation 4cb2ca6f-924e-4292-a411-ce10f8cc8f47 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Scaling Laws for Differentially Private Language Models Gemma 2: Improving Open Language Models at a Practical Size

Reference 35

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no resolver link, observed 2026-08-09T22:05:02.563895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.563895Z digest=sha256:4bb3c1a06b1c2034fe1592a322edb36a5ad1d0350e0da6da0f79dd033b496b2a

Observation bc758505-3f81-4876-a21b-66ee7e1a0cb8 · outbound

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

Scaling Laws for Differentially Private Language Models Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.566936Z digest=sha256:160ed0832ff24f1a16c5726f4abe3935b4e129062b3e85f38defa665f3d294f6

Observation 51b4e952-24b9-47a9-92a5-e08d6f6b7bc2 · outbound

This paper cites and Latonero, M.

Scaling Laws for Differentially Private Language Models and Latonero, M

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.601864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.570165Z digest=sha256:f05c036a5d39f26c283d591ce5227ec5034d584de8429055b5791ce10455d597

Observation 531f4b89-b57e-4725-9399-fafa7517f89f · outbound

This paper cites Google's differential privacy libraries., 2022.

Scaling Laws for Differentially Private Language Models Google's differential privacy libraries., 2022

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.593009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.573081Z digest=sha256:7ad2f9cab96a0590646321593970fa72d3c4701275cd9e6bf30654900d2ba583

Observation 204ccb5c-3c55-4daa-bada-596d6f559990 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Scaling Laws for Differentially Private Language Models Training Compute-Optimal Large Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.576030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.576030Z digest=sha256:ff29f2e437587d648b4ba3f96c21d627416cf8ada2dc768225b8d01342d85367

Observation 9ab250db-00ac-4786-9ab7-0eb61115ac32 · outbound

This paper cites T., Zhang, C., Li, Z., Li, B., and Wang, Z.

Scaling Laws for Differentially Private Language Models T., Zhang, C., Li, Z., Li, B., and Wang, Z

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.584233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.579167Z digest=sha256:acd973a7b25389dfd5013a3f969c46476bcc33b4cbeaf2e9c33475021af7aeaf

Observation efcf04e7-7395-4d3f-a199-0eff39f8a78b · outbound

This paper cites an unresolved cited work.

Scaling Laws for Differentially Private Language Models Unresolved cited work

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.582136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.582136Z digest=sha256:4a889cd9b3161d3ad65d8b46e63256f884e251d18c6378dc055a6c1260c40e5e

Observation e246c354-c05c-45b2-8cd9-db0c8346a679 · outbound

This paper cites Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy.

Scaling Laws for Differentially Private Language Models Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.585035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.585035Z digest=sha256:f27365b96a47c8aaf5daa1939c87c4813ad6285847c310d2403136a8aa95236a

Observation 610698f1-82a2-4550-b270-86ea014c0dba · outbound

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

Scaling Laws for Differentially Private Language Models Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.588331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.588331Z digest=sha256:5fe075e7cba78d8153b5ea5042222558788fb05c94eaa758f8fa5e5df047e9d4

Observation 3b8f90fa-1f90-4755-8bcc-6ff6a9fb6b59 · outbound

This paper cites Beyond the calibration point: Mechanism comparison in differential privacy.

Scaling Laws for Differentially Private Language Models Beyond the calibration point: Mechanism comparison in differential privacy

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.570636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.591575Z digest=sha256:a5f092ed30f321dc5380aa5e8f3e9f08b5ea80cfeabd72db50f7fae7a17a6f9a

Observation 9010c1b9-ea61-4ba5-9d2d-a4474def2505 · outbound

This paper cites Scaling Laws for Neural Language Models.

Scaling Laws for Differentially Private Language Models Scaling Laws for Neural Language Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.594525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.594525Z digest=sha256:3e2791a6dd12d49019e305119101e114a54ca20d17e8f2ad0efb70f6a645c2ab

Observation c916c46d-e4b3-4813-aa2d-8e886573fe4e · outbound

This paper cites an unresolved cited work.

Scaling Laws for Differentially Private Language Models Unresolved cited work

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.597755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.597755Z digest=sha256:11933d01e0fd2241a08394a6d2ac35d3a7de39c2c4fa9d8f6379543040a18095

Observation e04b89ba-cf82-455d-8e87-1fbc40ecf757 · outbound

This paper cites and Ponomareva, N.

Scaling Laws for Differentially Private Language Models and Ponomareva, N

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.556608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.600675Z digest=sha256:1a9f0fa2dd7f20f9141d5cd84f33ac2844a3dacb5e392e1d568abfccd09ca074

Observation 1494b815-bea2-4346-a848-0da50d6038ac · outbound

This paper cites Toward Training at ImageNet Scale with Differential Privacy.

Scaling Laws for Differentially Private Language Models Toward Training at ImageNet Scale with Differential Privacy

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.603557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.603557Z digest=sha256:224a658d9cedaea3fe5959d46106dabd4ef00caf0310c0028b18c22830a3ab5f

Observation 3ea0bf12-00c5-4c98-99fc-9ba54567ef65 · outbound

This paper cites Large language models can be strong differentially private learners.

Scaling Laws for Differentially Private Language Models Large language models can be strong differentially private learners

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.547312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.606708Z digest=sha256:8b9772259b238d6107f053311357608445d3d7c1147412b9c6d352fe7a83768f

Observation 1b9ec1d5-c3ae-46f4-ad65-f9684a0ff5d0 · outbound

This paper cites J., Novak, R., Lee, J., Wortsman, M., Xiao, L., Everett, K., Alemi, A.

Scaling Laws for Differentially Private Language Models J., Novak, R., Lee, J., Wortsman, M., Xiao, L., Everett, K., Alemi, A

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.538451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.609764Z digest=sha256:46c8577243dbffe2dd5bcb9bd6881bc56bd8abd324f36d44a840e2a53982389e

Observation d814cd33-f1f7-40e9-a583-81e4d4b5e092 · outbound

This paper cites Decoupled Weight Decay Regularization.

Scaling Laws for Differentially Private Language Models Decoupled Weight Decay Regularization

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.612675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.612675Z digest=sha256:d3e45802ba220e44c095b152bc300b9e804ed1ad858fa6053e53fc4e7950c1e7

Observation 3c33765d-4565-45cf-b9da-55ed2a7fbd24 · outbound

This paper cites Analyzing leakage of personally identifiable information in language models.

Scaling Laws for Differentially Private Language Models Analyzing leakage of personally identifiable information in language models

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.529460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.615787Z digest=sha256:3cdb4b0aff1b169de090fb1beb9bb4d3702b6a7ff531c4e19449dc23d92b2698

Observation e409d138-deec-47c0-ab74-44f5c6817c87 · outbound

This paper cites An Empirical Model of Large-Batch Training.

Scaling Laws for Differentially Private Language Models An Empirical Model of Large-Batch Training

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.618642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.618642Z digest=sha256:fc5f9cb3d16f493728f0fd3fa33c26788b2dfdd5dfe54df53bf4613e048aa523

Observation 0d005d45-91d4-4feb-8b38-a8ecedfa403b · outbound

This paper cites Updating quasi- N ewton matrices with limited storage.

Scaling Laws for Differentially Private Language Models Updating quasi- N ewton matrices with limited storage

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.520704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.621389Z digest=sha256:fcae2f736eef26ff5763e424f6677a47f78ff8e5903237a2570739847fcfa084

Observation 23fd9076-2f1c-4476-a358-4c62dc875249 · outbound

This paper cites and Wright, S.

Scaling Laws for Differentially Private Language Models and Wright, S

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.623885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.623885Z digest=sha256:f8ea1a92d9c95c5c5f7993297176e3ba93b8e68c480a6c662a46e9c90e80a4f4

Observation 1267a439-115e-465a-82ed-fb596b3bfa8a · outbound

This paper cites B., Vassilvitskii, S., Chien, S., and Thakurta, A.

Scaling Laws for Differentially Private Language Models B., Vassilvitskii, S., Chien, S., and Thakurta, A

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.507387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.626275Z digest=sha256:713185d0a76ffef1d5ba4a791bdf4875754ade93f23e22f5d438b39e0b51ce67

Observation 3a068439-a5ae-4f84-beb5-c6c501f4d11d · outbound

This paper cites Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon.

Scaling Laws for Differentially Private Language Models Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.628551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.628551Z digest=sha256:3c1a2f9e30fef549caa5368205018cfde5f54bcce804b0420b05d17bce883327

Observation 328571d9-525e-4b7b-9ea7-457250121db5 · outbound

This paper cites K., Charles, Z., Garrett, Z., Augenstein, S., and Mitchell, N.

Scaling Laws for Differentially Private Language Models K., Charles, Z., Garrett, Z., Augenstein, S., and Mitchell, N

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.499398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.631380Z digest=sha256:9cca63f3fb4113e870b6cf38ee5965db74d6ef2b985ef98846c4c18b635d65df

Observation 6464e725-0401-46ea-8f17-25a1de3c0133 · outbound

This paper cites TAN without a burn: Scaling laws of DP-SGD.

Scaling Laws for Differentially Private Language Models TAN without a burn: Scaling laws of DP-SGD

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.492205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.633800Z digest=sha256:dc92e617ff7edecedc0b2fb8934f4f98864d32edac423f8db16a9d632906fa8c

Observation d0cfe463-e430-4f28-90a4-893e72b6e568 · outbound

This paper cites Differentially private representation learning via image captioning.

Scaling Laws for Differentially Private Language Models Differentially private representation learning via image captioning

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.484688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.636162Z digest=sha256:ddda6893e777b6335e88b28a9fc42ff7c7216bf9f16ff72b5a350df74335ced0

Observation 3008b1f9-25be-4af8-bd40-f9250f46eccd · outbound

This paper cites J., Lee, J., Antognini, J., Sohl-Dickstein, J., Frostig, R., and Dahl, G.

Scaling Laws for Differentially Private Language Models J., Lee, J., Antognini, J., Sohl-Dickstein, J., Frostig, R., and Dahl, G

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.477492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.639070Z digest=sha256:c6e9328125ddb305998aa39ad02060ecde8e3594c9473b0d7cf547c368e50e5f

Observation 1384ed1c-bed2-48aa-9f17-5136093606e1 · outbound

This paper cites M., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P.

Scaling Laws for Differentially Private Language Models M., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.469264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.642090Z digest=sha256:d5ed8afc0c6f8670b273d1ac9d6cd3585b7a6843fa5edec1f22b70852189ae51

Observation 3ce99226-749d-4e4b-b4d1-a8f26831a0ce · outbound

This paper cites Enabling fast differentially private SGD via just-in-time compilation and vectorization.

Scaling Laws for Differentially Private Language Models Enabling fast differentially private SGD via just-in-time compilation and vectorization

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.459922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.644878Z digest=sha256:a6b81b98ea89bfd975ea9ba7494c0a7ee6cc3e855bedbc28e01c6d799d660385

Observation adb3c8f8-4dc9-48b7-a369-33646690c82d · outbound

This paper cites A., Manoel, A., Mireshghallah, F., Lin, Z., Gopi, S., Kulkarni, J., and Sim, R.

Scaling Laws for Differentially Private Language Models A., Manoel, A., Mireshghallah, F., Lin, Z., Gopi, S., Kulkarni, J., and Sim, R

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.451500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.647860Z digest=sha256:8607506f1d64bef6221e3d844d00d6103500747471d6c33422f6eacc9809a28f

Observation f48ce78d-8dc2-4d2d-b154-1fe5bc47d4c0 · outbound

This paper cites On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift.

Scaling Laws for Differentially Private Language Models On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift

Reference 65

Resolution
verified exact
local_arxiv, observed 2026-08-09T22:05:15.190444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.650727Z digest=sha256:e39230bea3a6137a460f9cdc6e5e5e4fd748102d23fae7ccfb143450e0a9cae8

Observation 8d59107f-16a4-483d-a043-de32d8e405f4 · outbound

This paper cites E., and Honkela, A.

Scaling Laws for Differentially Private Language Models E., and Honkela, A

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.442738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.653994Z digest=sha256:e38d1ec1b1c77572659848dc69a7645b18907e58270234e0115a7e29f12310f5

Observation 3e6b039f-e0e9-4307-8682-b74301c7a1d4 · outbound

This paper cites Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining.

Scaling Laws for Differentially Private Language Models Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.656929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.656929Z digest=sha256:91a3004de8dbbeaee9189e925ff66f51f45c92a676df5c73dec97ea998df8f5c

Observation 4de23078-6895-4f6e-8bdd-7e7ede5bdd97 · outbound

This paper cites Can public large language models help private cross-device federated learning? In NAACL (Findings), pp.\ 934--949, 2024.

Scaling Laws for Differentially Private Language Models Can public large language models help private cross-device federated learning? In NAACL (Findings), pp.\ 934--949, 2024

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.434369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.660157Z digest=sha256:895bb71a405a0f022dbb7f4be38123a73fd48384566a1ec1d64caf6a2f31074d

Observation fcc6f820-d944-42ff-8df4-a20d53e86eeb · outbound

This paper cites A., Backurs, A., Chandrasekaran, V., Kulkarni, J., and Sim, R.

Scaling Laws for Differentially Private Language Models A., Backurs, A., Chandrasekaran, V., Kulkarni, J., and Sim, R

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.425696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.662921Z digest=sha256:bbf7016da98371693dec3ad2cbb7d1c9ad90cc837bd07123c29856d7490957cb

Observation 1456e916-fcb1-4490-8c84-c2353aaf0761 · outbound

This paper cites T., and Mittal, P.

Scaling Laws for Differentially Private Language Models T., and Mittal, P

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.417064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.665791Z digest=sha256:b9eeaf6c5f556f6f3d6dcd3e5a3f7c58bbc451290bb3863f2669701847610687

Observation 785a2d0d-66e1-4d3c-9ee6-5d663f5d3811 · outbound

This paper cites GSPMD: General and Scalable Parallelization for ML Computation Graphs.

Scaling Laws for Differentially Private Language Models GSPMD: General and Scalable Parallelization for ML Computation Graphs

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.668521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.668521Z digest=sha256:9f4a780e91ce438f2a353d408a9b76b25e193612cf9be15f8196f259cee52364

Observation acad16a9-4871-4dad-ae95-9e512b23cf0e · outbound

This paper cites Privacy risk in machine learning: Analyzing the connection to overfitting.

Scaling Laws for Differentially Private Language Models Privacy risk in machine learning: Analyzing the connection to overfitting

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.408488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.671533Z digest=sha256:afe37686431e53cc2c18dabb87584c750943fa6171c74d1ea9465604747cfd59

Observation ae6a4999-2331-4545-b12d-2f53c6e34b32 · outbound

This paper cites Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.

Scaling Laws for Differentially Private Language Models Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.674730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.674730Z digest=sha256:10e1eb86c7e2ddbcc6905e538930d3639556adc14aaf58595e55bf68ff582cbf

Observation 9bda54f3-4a31-461f-9aab-46c8fabe2c16 · outbound

This paper cites Large scale private learning via low-rank reparametrization.

Scaling Laws for Differentially Private Language Models Large scale private learning via low-rank reparametrization

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.399002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.678274Z digest=sha256:b195acb696cc4ca9b3259ea5a7c768ee8c4d261a26e573a375025fa05e143c42

Observation e1567c43-320b-452b-afee-0783d63cbdaa · outbound

This paper cites A., Kamath, G., Kulkarni, J., Lee, Y.

Scaling Laws for Differentially Private Language Models A., Kamath, G., Kulkarni, J., Lee, Y

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.390083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.681016Z digest=sha256:601e371e154a59fbff9cbeb766d207b85c6e1df241851f54b40abc4b2abc582b

Observation 57bdad84-9478-4da6-a432-c3db2ea83848 · outbound

This paper cites How Does Critical Batch Size Scale in Pre-training?.

Scaling Laws for Differentially Private Language Models How Does Critical Batch Size Scale in Pre-training?

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.683938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.683938Z digest=sha256:1d2f2e0c7e08de55927cedb6f09b22ce3bce5e49415f7ecf39912e1ac44a6ef7

Observation 5b729103-da47-480e-b2cd-3dd4e3d335e0 · outbound

This paper cites K., Oh, S., and He, N.

Scaling Laws for Differentially Private Language Models K., Oh, S., and He, N

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.381145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.687014Z digest=sha256:3097922d858a5f9e1803061d4b578c3c4db3d885ea6a277c1299ca7cf9c86c3a

Observation abae21ff-797f-45b8-98fd-2434ea18dcb5 · outbound

This paper cites Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach.

Scaling Laws for Differentially Private Language Models Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach

Reference 78

Resolution
verified exact
local_arxiv, observed 2026-08-09T22:05:15.146944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.689970Z digest=sha256:e8a3eeb6be060a5da0c5783c42a3dc2f6c1b5e4f77065fcc8cfbbcf5830dc080

Observation 44fbaa5d-8587-44e2-bccd-921f83b75673 · outbound

This paper cites S., Salakhutdinov, R., Urtasun, R., Torralba, A., and Fidler, S.

Scaling Laws for Differentially Private Language Models S., Salakhutdinov, R., Urtasun, R., Torralba, A., and Fidler, S

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.372689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.693008Z digest=sha256:50986490c97bc819bd1753824ef6adabe72fcbbabbda9a271e44757406a42f75

Observation c0cf2b95-fa0a-4822-aff9-116a852b705b · outbound

This paper cites T., Stieger, S., Feiner, L.

Scaling Laws for Differentially Private Language Models T., Stieger, S., Feiner, L

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:05:15.364570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T22:05:02.695877Z digest=sha256:90de7f5ecdadd4aecae772ee099b5d9d13d3bbdb315a5e27a18791094631be21

Observation b3df63b0-23a1-4706-8995-da539e38e24a · outbound

This paper cites @esa (Ref.

Scaling Laws for Differentially Private Language Models @esa (Ref

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.698766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.698766Z digest=sha256:a2a1ed8d2a09edc6e449897fc996b3a9de100875fa2d8fa0a6ee968aca0186a7

Observation e0080499-9f74-4ca0-b91a-d6e54c28abda · outbound

This paper cites an unresolved cited work.

Scaling Laws for Differentially Private Language Models Unresolved cited work

Reference 82

Resolution
unresolved
no resolver link, observed 2026-08-09T22:05:02.702159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.702159Z digest=sha256:d43b3ad3b087981c559e452956a246d58377b8fdf06fd4f335453346d4a3508d

Observation dd26519d-dd4f-4072-a1eb-9650c2524066 · outbound

This paper cites bG g6b嗍 3kQI @k /h m?hlKJڅ:| 4 j 2M^ ; Z ݄ hT2 !; & ȯ ɾD :] q u ` bcߩ -@n- e5 h v Vb?SHP r! 5 ШEw7wlQ # `K.

Scaling Laws for Differentially Private Language Models bG g6b嗍 3kQI @k /h m?hlKJڅ:| 4 j 2M^ ; Z ݄ hT2 !; & ȯ ɾD :] q u ` bcߩ -@n- e5 h v Vb?SHP r! 5 ШEw7wlQ # `K

Reference 83

Resolution
malformed identifier
no resolver link, observed 2026-08-09T22:05:02.705232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:05:02.705232Z digest=sha256:8c09f94ca16f33d5a66075d400d893a0c8b06b0b6c3f99dfe27c91ba0be1d7ab

Pith citing papers

Observation 629c9eb3-66de-4892-a8d2-0135ea09d741 · inbound

High-Dimensional Private Linear Regression with Optimal Rates cites this paper.

High-Dimensional Private Linear Regression with Optimal Rates Scaling Laws for Differentially Private Language Models

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-22T02:50:58.335502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-22T02:50:09.196457Z digest=sha256:f89459febe12b66f902043b8fb8ea9f31ca51ce2820e99b47fc79e4aab78d402

Observation 3524c178-261c-4dfc-8770-f30d501c0f19 · inbound

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD cites this paper.

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Scaling Laws for Differentially Private Language Models

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-16T13:37:56.495543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T13:37:50.765735Z digest=sha256:ec80276c0062a901ab4eade96b86f95269c56a86dcf85942997c261403c7d898

Observation d3926d10-a519-42cb-a817-f435214fa1e7 · inbound

Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD? cites this paper.

Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD? Scaling Laws for Differentially Private Language Models

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-11T04:45:58.064210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-11T01:06:21.633242Z digest=sha256:9cb0b030cc6b7ffb490bd4125298c93f69c4c559afc7fa164cc2a599c54744c3