Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-08T19:09:04.178117Z
Paper Citation Record · LEDGER
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model
As of 18 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 6 inbound Pith citation observations for arXiv:2502.05505.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:09:46.562919Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T20:57:23.708566Z
Reference resolution
71 of 71 outbound references displayed
- verified exact0
- verified fuzzy34
- unresolved37
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation cca315e0-0434-4af5-bc61-93c178718088 · outbound
This paper cites The crossroads of innovation and privacy: Private synthetic data for generative ai.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model The crossroads of innovation and privacy: Private synthetic data for generative ai
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:06.095889Z
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 38d49b8c-310d-4e09-a058-c4bef3694b9c · outbound
This paper cites Understanding aggregate trends for apple intelligence using dif- ferential privacy.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Understanding aggregate trends for apple intelligence using dif- ferential privacy
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:06.066167Z
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 008e9c26-a3f5-4fd6-a0ff-4da2808feeca · outbound
This paper cites Genesis: A universal and generative physics engine for robotics and beyond, December 2024.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Genesis: A universal and generative physics engine for robotics and beyond, December 2024
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:06.004582Z
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 cfa96a6b-4d4c-4ee3-8031-39a6f4ae7cd3 · outbound
This paper cites Digiface-1m: 1 million digital face images for face recognition.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Digiface-1m: 1 million digital face images for face recognition
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.833993Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8832febe-2dc2-4305-baac-f71d0fc9fd6b · outbound
This paper cites Privacy-preserving generative deep neural networks sup- port clinical data sharing.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Privacy-preserving generative deep neural networks sup- port clinical data sharing
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.977898Z
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 480d3c3f-c7ec-4a6a-acd1-3b4f190e75d8 · outbound
This paper cites Improving image generation with better captions.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Improving image generation with better captions
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.844439Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c07611a7-6e23-4158-8140-49bfd6d1e244 · outbound
This paper cites Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.850176Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2084d707-49a2-451d-afd4-dc29ba66a903 · outbound
This paper cites Don’t generate me: Training differentially private generative models with sinkhorn divergence.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Don’t generate me: Training differentially private generative models with sinkhorn divergence
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.917409Z
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 d90e8b04-aade-427b-afcb-94ff5d9c974d · outbound
This paper cites GS-WGAN: A gradient-sanitized approach for learning differentially private generators.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model GS-WGAN: A gradient-sanitized approach for learning differentially private generators
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.899626Z
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 90bcbf36-cf10-4d42-ba72-e82293281755 · outbound
This paper cites Dpgen: Differentially private generative energy-guided network for natural image synthesis.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Dpgen: Differentially private generative energy-guided network for natural image synthesis
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.883676Z
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 f3259517-4a27-4e1d-bbf8-79eebad0592b · outbound
This paper cites Blender - a 3D modelling and rendering package.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Blender - a 3D modelling and rendering package
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.762466Z
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 a6242575-f65c-4494-8693-51b975a9292d · outbound
This paper cites Meta-sim2: Unsupervised learning of scene structure for synthetic data generation.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Meta-sim2: Unsupervised learning of scene structure for synthetic data generation
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.746907Z
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 4203c844-6558-45d9-9114-c2c4afd456be · outbound
This paper cites Differentially private diffusion models.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Differentially private diffusion models
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.730886Z
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 f97c9f3d-c8f5-4f30-9117-c893a9e049c1 · outbound
This paper cites Differentially Private Diffusion Models.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Differentially Private Diffusion Models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.884887Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed7aeb9c-6bcf-4be3-af47-691f08ca759a · outbound
This paper cites Calibrating noise to sensitivity in private data analysis.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Calibrating noise to sensitivity in private data analysis
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.889965Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 33ed8f34-ab1d-4e32-bee0-2a1b96d06e24 · outbound
This paper cites The algorithmic foundations of differential privacy.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model The algorithmic foundations of differential privacy
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.894485Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 54684dd7-a446-4acb-9665-93c9de3eb8a0 · outbound
This paper cites A framework for the quantitative evaluation of disentangled representations.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model A framework for the quantitative evaluation of disentangled representations
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.899039Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ba8b814e-b00c-4463-a62f-1675dc43c173 · outbound
This paper cites Unreal engine.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Unreal engine
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.633405Z
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 0657662f-3a79-4848-b1a2-ea7ecfb7d8f7 · outbound
This paper cites python avatars.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model python avatars
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.617478Z
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 debbf19e-4b1b-4479-9290-ba1c8306e427 · outbound
This paper cites Differentially Private Diffusion Models Generate Useful Synthetic Images.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Differentially Private Diffusion Models Generate Useful Synthetic Images
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.919231Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c8ead60e-8e9b-4027-b989-39e886c388db · outbound
This paper cites DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.923920Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 02a2403b-c6ae-4a2f-9373-9768278c2002 · outbound
This paper cites Generative adversarial networks.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Generative adversarial networks
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.929893Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 638cbf1c-3b87-4261-b4b5-f41dba40daea · outbound
This paper cites Google fonts.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Google fonts
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.574304Z
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 05436bdd-2335-466c-9119-ac76de4a1251 · outbound
This paper cites an unresolved cited work.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Unresolved cited work
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.939225Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 46867e81-c2ec-4d55-b82e-e5cc9a692b45 · outbound
This paper cites DP-MERF: differentially private mean embeddings with random features for practical privacy-preserving data generation.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model DP-MERF: differentially private mean embeddings with random features for practical privacy-preserving data generation
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.491728Z
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 424777e5-2ad7-4c4b-a83a-af991a9b06d0 · outbound
This paper cites Dp-merf: Differentially private mean embeddings with randomfeatures for practical privacy-preserving data generation.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Dp-merf: Differentially private mean embeddings with randomfeatures for practical privacy-preserving data generation
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.474731Z
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 bc3a528e-f1c7-4b5a-8d23-f63bf284df61 · outbound
This paper cites Pre-trained perceptual features improve differentially private image generation.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Pre-trained perceptual features improve differentially private image generation
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.360755Z
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 26d5f486-dbda-4261-adce-58f442d499ba · outbound
This paper cites Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.959979Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b904212a-8cee-43cd-945f-de415d3d2cb3 · outbound
This paper cites Deep residual learning for image recognition.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Deep residual learning for image recognition
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.965480Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fce3e84f-27d6-4abd-907c-8a1aa7260062 · outbound
This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Gans trained by a two time-scale update rule converge to a local nash equilibrium
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.969930Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 13b8e8c8-ef4c-4267-8d26-9845b449a38e · outbound
This paper cites PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.974510Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 20f93e2d-e668-407f-8416-6b573769400c · outbound
This paper cites POPri: Private Federated Learning using Preference-Optimized Synthetic Data.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model POPri: Private Federated Learning using Preference-Optimized Synthetic Data
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.980412Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 06538939-befc-430c-80b2-9ef37af68b4f · outbound
This paper cites Sok: Privacy-preserving data synthesis.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Sok: Privacy-preserving data synthesis
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.321455Z
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 c626a896-39d1-4e8e-9247-463ff7416835 · outbound
This paper cites Introduction to network simulator 2 (NS2).
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Introduction to network simulator 2 (NS2)
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.306478Z
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 edf7ff9b-8de4-498b-9032-37591847f82d · outbound
This paper cites Functional renyi differential privacy for generative modeling.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Functional renyi differential privacy for generative modeling
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.291030Z
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 197af3dc-1249-4e04-b067-b601e3d342fc · outbound
This paper cites PATE-GAN: Generating synthetic data with differential privacy guarantees.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model PATE-GAN: Generating synthetic data with differential privacy guarantees
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.274599Z
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 23ab83f4-3ba4-4e40-8e0a-e36e203923c9 · outbound
This paper cites Meta-sim: Learning to generate synthetic datasets.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Meta-sim: Learning to generate synthetic datasets
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.242119Z
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 0cd39d6c-3c54-4e5d-a5d1-d4a9d1fff362 · outbound
This paper cites Learning multiple layers of features from tiny images.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Learning multiple layers of features from tiny images
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.010829Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b12be946-7acb-4160-a191-717d49080b18 · outbound
This paper cites The mnist database of handwritten digits.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model The mnist database of handwritten digits
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.015600Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 53682953-2b2b-42d2-9764-e377ab692f18 · outbound
This paper cites PrivImage: Differentially private synthetic image generation using diffusion models with Semantic-Aware pretraining.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model PrivImage: Differentially private synthetic image generation using diffusion models with Semantic-Aware pretraining
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.194373Z
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 ab0deb58-801c-4e8b-b766-682eeca27769 · outbound
This paper cites Large Language Models Can Be Strong Differentially Private Learners.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Large Language Models Can Be Strong Differentially Private Learners
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.025195Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8cd905d4-092e-4131-bcf1-867f94e651f7 · outbound
This paper cites Data Sharing with Generative Adversarial Networks: From Theory to Practice.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Data Sharing with Generative Adversarial Networks: From Theory to Practice
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.178282Z
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 3579cd90-f5fe-4216-ace0-2b866e4b5b10 · outbound
This paper cites Differentially private synthetic data via APIs 3: Using simulators instead of foundation model.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Differentially private synthetic data via APIs 3: Using simulators instead of foundation model
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.126558Z
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 f61325a2-a323-43fc-aa50-c77aebc72850 · outbound
This paper cites Differentially private synthetic data via APIs 3: Using simulators instead of foundation model.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Differentially private synthetic data via APIs 3: Using simulators instead of foundation model
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.108877Z
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 2d176ff9-9040-4851-b167-94253f39e10b · outbound
This paper cites Differen- tially private synthetic data via foundation model APIs 1: Images.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Differen- tially private synthetic data via foundation model APIs 1: Images
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.093945Z
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 77f3c09b-05b3-478b-acce-f2ef44abd04a · outbound
This paper cites Using gans for sharing networked time series data: Challenges, initial promise, and open questions.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Using gans for sharing networked time series data: Challenges, initial promise, and open questions
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.042066Z
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 b78062dc-82cc-4b3b-b794-5b9fb9c3e182 · outbound
This paper cites Infogan-cr and modelcen- trality: Self-supervised model training and selection for disentangling gans.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Infogan-cr and modelcen- trality: Self-supervised model training and selection for disentangling gans
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:05.013700Z
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 e413053c-2bee-400d-abc0-a6184301e6d7 · outbound
This paper cites Distilled decoding 1: One-step sampling of image auto-regressive models with flow matching.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Distilled decoding 1: One-step sampling of image auto-regressive models with flow matching
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.059667Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed52fb20-250c-41be-848d-dc2c141d5a35 · outbound
This paper cites Liu, Saiyue Lyu, Margarita Vinaroz, and Mijung Park.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Liu, Saiyue Lyu, Margarita Vinaroz, and Mijung Park
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:04.989103Z
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 5f8c6f25-1d9f-4e63-988c-f2f0cc1836a3 · outbound
This paper cites Deep learning face attributes in the wild.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Deep learning face attributes in the wild
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.069132Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 931e915b-1aa8-4ad3-988e-bce2f204dc02 · outbound
This paper cites Gpt-4 technical report, 2023.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Gpt-4 technical report, 2023
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.074217Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ef7f4d05-43b6-4627-85dc-8e8d1c821470 · outbound
This paper cites The ns-3 network simulator.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model The ns-3 network simulator
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:04.930869Z
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 66a3134a-54e4-4867-bed2-57ae739e1d15 · outbound
This paper cites High- resolution image synthesis with latent diffusion models.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model High- resolution image synthesis with latent diffusion models
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.083143Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 20c54355-3ce7-4385-8943-032f52105562 · outbound
This paper cites Improved techniques for training gans.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Improved techniques for training gans
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.087689Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dd1ddf00-2122-4b9d-a6fc-c6cdd55abebd · outbound
This paper cites Deep unsuper- vised learning using nonequilibrium thermodynamics.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Deep unsuper- vised learning using nonequilibrium thermodynamics
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.092832Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 879fd24d-a9dd-47cb-8c54-e9e715598013 · outbound
This paper cites Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.098017Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f2203801-bc4a-48fe-a22d-b084f3506eef · outbound
This paper cites Benchmarking Differentially Private Synthetic Data Generation Algorithms.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Benchmarking Differentially Private Synthetic Data Generation Algorithms
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.103026Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 331ee883-6c7c-4952-8288-00398f28916b · outbound
This paper cites Differentially Private Fine-Tuning of Diffusion Models.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Differentially Private Fine-Tuning of Diffusion Models
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.107717Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d4ac8b55-753d-4b9c-ab67-71b778a128e4 · outbound
This paper cites Hermite polynomial features for private data generation.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Hermite polynomial features for private data generation
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:04.873680Z
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 cb4dfcc6-503c-418a-a76b-9ce94af159f9 · outbound
This paper cites Fake it till you make it: face analysis in the wild using synthetic data alone.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Fake it till you make it: face analysis in the wild using synthetic data alone
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:04.840244Z
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 6d2f7cf6-5da9-4c9b-ba42-4eecf62ded90 · outbound
This paper cites Differentially Private Synthetic Data via Foundation Model APIs 2: Text.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.122991Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 62836a38-a7fb-4ea9-afe8-af60a4c42927 · outbound
This paper cites Differentially Private Generative Adversarial Network.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Differentially Private Generative Adversarial Network
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.127961Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 19fd2faf-4f0c-4a36-a42a-fc39c52aa6b3 · outbound
This paper cites Aggregated residual transformations for deep neural networks.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Aggregated residual transformations for deep neural networks
Reference 64
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.133407Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f8bb1c4e-8309-42d6-92eb-63b3bedafada · outbound
This paper cites Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.138404Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bad07525-ed55-4b0f-b981-278d8a25659b · outbound
This paper cites Practical gan-based synthetic ip header trace generation using netshare.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Practical gan-based synthetic ip header trace generation using netshare
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:04.810411Z
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 a7aba2a3-2edb-45b0-97ac-c4963f8f7a81 · outbound
This paper cites Selective Pre-training for Private Fine-tuning.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Selective Pre-training for Private Fine-tuning
Reference 67
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.148957Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 787fbcae-fd71-4007-b63b-82e2689ea026 · outbound
This paper cites Differentially Private Fine-tuning of Language Models.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Differentially Private Fine-tuning of Language Models
Reference 68
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.155299Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ea164c55-a318-4476-9eeb-9d0c5aeea0e4 · outbound
This paper cites Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
Reference 69
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.161833Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9e5bfcfb-b435-46a3-afc3-13519af1878d · outbound
This paper cites Wide Residual Networks.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Wide Residual Networks
Reference 70
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.166902Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 74ef547b-8897-41ea-b72f-c65743497872 · outbound
This paper cites Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion
Reference 71
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:04.171855Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 83d8d2c6-b166-4477-8216-1829b92a6c90 · outbound
This paper cites Following [22], we set the number of generated samples to be 60,000.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Following [22], we set the number of generated samples to be 60,000
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:09:04.789872Z
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
Observation 8165f8fa-5f6f-47a9-980f-8bc2840934e7 · inbound
POPri: Private Federated Learning using Preference-Optimized Synthetic Data cites this paper.
POPri: Private Federated Learning using Preference-Optimized Synthetic Data Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:09:46.562919Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1353c80c-6876-48df-8833-a197b5089d4e · inbound
Clustering and Median Aggregation Improve Differentially Private Inference cites this paper.
Clustering and Median Aggregation Improve Differentially Private Inference Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:47:28.360138Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb5eb492-4961-46e0-98cf-38b4d7c863cb · inbound
Struct-Bench: A Benchmark for Differentially Private Structured Text Generation cites this paper.
Struct-Bench: A Benchmark for Differentially Private Structured Text Generation Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:57:47.617364Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation acdaeed0-c101-45b4-97d4-5b2ff20e9d4c · inbound
DP-SAPF: Saliency-Aware Parameter Fine-tuning of Public Models for Differentially Private Image Synthesis cites this paper.
DP-SAPF: Saliency-Aware Parameter Fine-tuning of Public Models for Differentially Private Image Synthesis Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model
Reference 4
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T14:33:30.898981Z
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 4c6e5d1c-a1bc-4d6c-a290-612aeb788d18 · inbound
Differentially Private Synthetic Data via APIs 4: Tabular Data cites this paper.
Differentially Private Synthetic Data via APIs 4: Tabular Data Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T20:57:23.710281Z
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 4034cf1c-1193-4292-b784-c097a09ef73d · inbound
Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data cites this paper.
Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T11:15:50.140301Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.