Cover Image

GEM+: Scalable State-of-the-Art Private Synthetic Data with Generator Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Maddock, Samuel, Gade, Shripad, Cormode, Graham, Bullock, Will
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation
by: Maddock, Samuel, et al.
Published: (2025)

Synthetic Tabular Data: Methods, Attacks and Defenses
by: Cormode, Graham, et al.
Published: (2025)

FLAIM: AIM-based Synthetic Data Generation in the Federated Setting
by: Maddock, Samuel, et al.
Published: (2023)

Private Federated Multiclass Post-hoc Calibration
by: Maddock, Samuel, et al.
Published: (2025)

Towards Robust Federated Analytics via Differentially Private Measurements of Statistical Heterogeneity
by: Scott, Mary, et al.
Published: (2024)

Power Transform Revisited: Numerically Stable, and Federated
by: Xu, Xuefeng, et al.
Published: (2025)

Federated Computation of ROC and PR Curves
by: Xu, Xuefeng, et al.
Published: (2025)

FedPS: Federated data Preprocessing via aggregated Statistics
by: Xu, Xuefeng, et al.
Published: (2026)

Distributed, communication-efficient, and differentially private estimation of KL divergence
by: Scott, Mary, et al.
Published: (2024)

PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality
by: Srinivas, Harish, et al.
Published: (2024)

Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?
by: Perez, Ileana Montoya, et al.
Published: (2024)

Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees
by: Lin, Shurong, et al.
Published: (2025)

Differentially Private Synthetic Data Generation for Relational Databases
by: Alimohammadi, Kaveh, et al.
Published: (2024)

A theoretical basis for model collapse in recursive training
by: Borkar, Vivek Shripad
Published: (2025)

GEM-T: Generative Tabular Data via Fitting Moments
by: Li, Miao, et al.
Published: (2025)

EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors
by: Banayeeanzade, Amin, et al.
Published: (2026)

Online Differentially Private Synthetic Data Generation
by: He, Yiyun, et al.
Published: (2024)

Stochastic approximation in non-markovian environments revisited
by: Borkar, Vivek Shripad
Published: (2026)

Latent Noise Injection for Private and Statistically Aligned Synthetic Data Generation
by: Shen, Rex, et al.
Published: (2025)

Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?
by: Swanberg, Marika, et al.
Published: (2025)

A dynamic view of some anomalous phenomena in SGD
by: Borkar, Vivek Shripad
Published: (2025)

Scalable and Privacy-Preserving Synthetic Data Generation on Decentralised Web
by: Ramesh, Vishal, et al.
Published: (2023)

Synthetic Data Generation and Differential Privacy using Tensor Networks' Matrix Product States (MPS)
by: R., Alejandro Moreno, et al.
Published: (2025)

Dynamic Context Evolution for Scalable Synthetic Data Generation
by: Lingo, Ryan, et al.
Published: (2026)

Differentially Private Synthetic Data Generation Using Context-Aware GANs
by: Kotal, Anantaa, et al.
Published: (2025)

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training
by: Choi, Yujin, et al.
Published: (2024)

Controlled Generation for Private Synthetic Text
by: Zhao, Zihao, et al.
Published: (2025)

SpinML: Customized Synthetic Data Generation for Private Training of Specialized ML Models
by: Zhang, Jiang, et al.
Published: (2025)

PRISM: Differentially Private Synthetic Data with Structure-Aware Budget Allocation for Prediction
by: Asiaee, Amir, et al.
Published: (2026)

Synthetic Interaction Data for Scalable Personalization in Large Language Models
by: Ma, Yuchen, et al.
Published: (2026)

PATE-TabTransGAN: Differentially Private Synthetic Tabular Data Generation via Transformer-Based Student Discrimination
by: Youssef, M., et al.
Published: (2026)

Differentially Private and Scalable Estimation of the Network Principal Component
by: Khayatian, Alireza, et al.
Published: (2025)

Evaluation in Neural Style Transfer: A Review
by: Ioannou, Eleftherios, et al.
Published: (2024)

Differentially Private Synthetic Data with Private Density Estimation
by: Bojkovic, Nikolija, et al.
Published: (2024)

Evaluation-Aware Reinforcement Learning
by: Deshmukh, Shripad Vilasrao, et al.
Published: (2025)

dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
by: Mahiou, Sofiane, et al.
Published: (2025)

Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
by: Fuentes, Miguel, et al.
Published: (2024)

Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics
by: Gu, Anming, et al.
Published: (2025)

zGAN: An Outlier-focused Generative Adversarial Network For Realistic Synthetic Data Generation
by: Azimi, Azizjon, et al.
Published: (2024)

SAVA: Scalable Learning-Agnostic Data Valuation
by: Kessler, Samuel, et al.
Published: (2024)