Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T22:05:02.705232Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T22:05:02.705232Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
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83 of 83 outbound references displayed
External citation measurements
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Scaling Laws for Differentially Private Language Models B., Mironov, I., Talwar, K., and Zhang, L
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Scaling Laws for Differentially Private Language Models GPT-4 Technical Report
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Scaling Laws for Differentially Private Language Models Unlocking Accuracy and Fairness in Differentially Private Image Classification
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Scaling Laws for Differentially Private Language Models A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., and Tram \`e r, F
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Scaling Laws for Differentially Private Language Models Fine-Tuning Large Language Models with User-Level Differential Privacy
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Scaling Laws for Differentially Private Language Models Symbolic Discovery of Optimization Algorithms
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Scaling Laws for Differentially Private Language Models Mind the privacy unit! user-level differential privacy for language model fine-tuning
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Scaling Laws for Differentially Private Language Models Unlocking High-Accuracy Differentially Private Image Classification through Scale
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Scaling Laws for Differentially Private Language Models BERT : Pre-training of deep bidirectional transformers for language understanding
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Scaling Laws for Differentially Private Language Models Flocks of stochastic parrots: Differentially private prompt learning for large language models
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Scaling Laws for Differentially Private Language Models The Llama 3 Herd of Models
Reference 29
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Scaling Laws for Differentially Private Language Models Calibrating noise to sensitivity in private data analysis
Reference 30
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Scaling Laws for Differentially Private Language Models Language models scale reliably with over-training and on downstream tasks
Reference 31
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Scaling Laws for Differentially Private Language Models Predictability and surprise in large generative models
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Scaling Laws for Differentially Private Language Models Gemini: A Family of Highly Capable Multimodal Models
Reference 33
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Scaling Laws for Differentially Private Language Models Gemma: Open Models Based on Gemini Research and Technology
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Scaling Laws for Differentially Private Language Models Gemma 2: Improving Open Language Models at a Practical Size
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Scaling Laws for Differentially Private Language Models Differentially Private Diffusion Models Generate Useful Synthetic Images
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Scaling Laws for Differentially Private Language Models and Latonero, M
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Scaling Laws for Differentially Private Language Models Training Compute-Optimal Large Language Models
Reference 39
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Scaling Laws for Differentially Private Language Models T., Zhang, C., Li, Z., Li, B., and Wang, Z
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Reference 41
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Scaling Laws for Differentially Private Language Models Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy
Reference 42
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Scaling Laws for Differentially Private Language Models Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy
Reference 43
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Scaling Laws for Differentially Private Language Models Toward Training at ImageNet Scale with Differential Privacy
Reference 48
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Scaling Laws for Differentially Private Language Models Large language models can be strong differentially private learners
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Scaling Laws for Differentially Private Language Models Decoupled Weight Decay Regularization
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Reference 52
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Scaling Laws for Differentially Private Language Models An Empirical Model of Large-Batch Training
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Scaling Laws for Differentially Private Language Models Updating quasi- N ewton matrices with limited storage
Reference 54
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Scaling Laws for Differentially Private Language Models and Wright, S
Reference 55
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Scaling Laws for Differentially Private Language Models Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon
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Scaling Laws for Differentially Private Language Models TAN without a burn: Scaling laws of DP-SGD
Reference 59
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Reference 62
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Reference 64
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Scaling Laws for Differentially Private Language Models E., and Honkela, A
Reference 66
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Reference 67
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Reference 68
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Reference 70
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Scaling Laws for Differentially Private Language Models GSPMD: General and Scalable Parallelization for ML Computation Graphs
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Reference 72
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Reference 73
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Reference 74
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Reference 75
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Reference 76
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Reference 77
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Reference 78
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Reference 79
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Reference 80
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Scaling Laws for Differentially Private Language Models @esa (Ref
Reference 81
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Reference 82
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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
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Observation 629c9eb3-66de-4892-a8d2-0135ea09d741 · inbound
High-Dimensional Private Linear Regression with Optimal Rates Scaling Laws for Differentially Private Language Models
Reference 19
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Observation 3524c178-261c-4dfc-8770-f30d501c0f19 · inbound
Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Scaling Laws for Differentially Private Language Models
Reference 47
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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation d3926d10-a519-42cb-a817-f435214fa1e7 · inbound
Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD? Scaling Laws for Differentially Private Language Models
Reference 17
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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.