most citedGRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

cs.LG2025

Uncertainty-Guided Model Selection for Tabular Foundation Models in Biomolecule Efficacy Prediction

Jie Li, Andrew McCarthy, Zhizhuo Zhang +1

In-context learners like TabPFN are promising for biomolecule efficacy prediction, where established molecular feature sets and relevant experimental results can serve as powerful…

cs.LG2022

Safe machine learning model release from Trusted Research Environments: The SACRO-ML package

Jim Smith, Richard J. Preen, Andrew McCarthy +8

We present SACRO-ML, an integrated suite of open source Python tools to facilitate the statistical disclosure control (SDC) of machine learning (ML) models trained on confidential…

cs.LG2022★ 2 cited

GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)

Emily Jefferson, James Liley, Maeve Malone +16

TREs are widely, and increasingly used to support statistical analysis of sensitive data across a range of sectors (e.g., health, police, tax and education) as they enable secure a…