SurvivalGAN: Generating Time-to-Event Data for Survival Analysis

1 code implementation • 24 Feb 2023 • Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Lio, Mihaela van der Schaar

SurvivalGAN outperforms multiple baselines at generating survival data, and in particular addresses the failure modes as measured by the new metrics, in addition to improving downstream performance of survival models trained on the synthetic data.

Fairness Survival Analysis

AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning

1 code implementation • 21 Oct 2022 • Fergus Imrie, Bogdan Cebere, Eoin F. McKinney, Mihaela van der Schaar

However, the use of machine learning introduces a number of technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings.

HyperImpute: Generalized Iterative Imputation with Automatic Model Selection

1 code implementation • 15 Jun 2022 • Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar

Consider the problem of imputing missing values in a dataset.

Imputation Model Selection

Syft 0.5: A Platform for Universally Deployable Structured Transparency

1 code implementation • 26 Apr 2021 • Adam James Hall, Madhava Jay, Tudor Cebere, Bogdan Cebere, Koen Lennart van der Veen, George Muraru, Tongye Xu, Patrick Cason, William Abramson, Ayoub Benaissa, Chinmay Shah, Alan Aboudib, Théo Ryffel, Kritika Prakash, Tom Titcombe, Varun Kumar Khare, Maddie Shang, Ionesio Junior, Animesh Gupta, Jason Paumier, Nahua Kang, Vova Manannikov, Andrew Trask

We present Syft 0. 5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems.

Privacy Preserving

TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption

2 code implementations • 7 Apr 2021 • Ayoub Benaissa, Bilal Retiat, Bogdan Cebere, Alaa Eddine Belfedhal

Machine learning algorithms have achieved remarkable results and are widely applied in a variety of domains.

BIG-bench Machine Learning Privacy Preserving

Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning

2 code implementations • 18 Nov 2020 • Nick Angelou, Ayoub Benaissa, Bogdan Cebere, William Clark, Adam James Hall, Michael A. Hoeh, Daniel Liu, Pavlos Papadopoulos, Robin Roehm, Robert Sandmann, Phillipp Schoppmann, Tom Titcombe

We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C).

BIG-bench Machine Learning Privacy Preserving