23 citations · 25 across the 5 of their papers we have counts for
5 papers
Generating Benchmark Health Data Using a Tabular Diffusion Transformer
Hao Yan, Lisa Pilgram, Dan Liu +3
Cross-Tabular Data Generation (CTDG) seeks to learn a generative model from multiple heterogeneous tables and produce new synthetic tabular datasets. However, existing synthetic ta…
SUP: An Inferable Private Multiple Testing Framework with Super Uniformity
Kehan Wang, Wenxuan Song, Wangli Xu +1
Multiple testing is widely applied across scientific fields, particularly in genomic and health data analysis, where protecting sensitive personal information is imperative. Howeve…
A Consensus Privacy Metrics Framework for Synthetic Data
Lisa Pilgram, Fida K. Dankar, Jorg Drechsler +12
Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals' privacy…
FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning
Jordan Slessor, Dezheng Kong, Xiaofen Tang +2
Federated learning (FL) is a machine learning methodology that involves the collaborative training of a global model across multiple decentralized clients in a privacy-preserving w…
Statistical Undersampling with Mutual Information and Support Points
Alex Mak, Shubham Sahoo, Shivani Pandey +2
Class imbalance and distributional differences in large datasets present significant challenges for classification tasks machine learning, often leading to biased models and poor p…