James E. Smith

Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities

2023 · 13 Zit. · Heliyon

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

2022 · 5 Zit. · arXiv (Cornell University)

Machine Learning Models Disclosure from Trusted Research Environments\n (TRE), Challenges and Opportunities

2021 · 4 Zit. · arXiv (Cornell University)

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

2022 · 2 Zit. · Zenodo (CERN European Organization for Nuclear Research)

Statistical disclosure controls for machine learning models

2021 · 2 Zit.

Machine Learning Models Disclosure from Trusted Research Environments (TRE), Challenges and Opportunities

2021 · 2 Zit. · arXiv (Cornell University)

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

2022 · 0 Zit. · Discovery Research Portal (University of Dundee)

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

2022 · 0 Zit. · arXiv (Cornell University)

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

2022 · 0 Zit. · Zenodo (CERN European Organization for Nuclear Research)