58 citations · 200 across the 12 of their papers we have counts for
6 papers · 1 filter
A Distribution-to-Distribution Neural Probabilistic Forecasting Framework for Dynamical Systems
Tianlin Yang, Hailiang Du, Louis Aslett
Probabilistic forecasts provide a principled representation of forecast uncertainty through predictive distributions. Directly propagating predictive distributions with conventiona…
Holdouts set for safe predictive model updating
Sami Haidar-Wehbe, Samuel R Emerson, Louis J M Aslett +1
Predictive risk scores for adverse outcomes are increasingly crucial in guiding health interventions. Such scores may need to be periodically updated due to change in the distribut…
Model updating after interventions paradoxically introduces bias
James Liley, Samuel R Emerson, Bilal A Mateen +3
Machine learning is increasingly being used to generate prediction models for use in a number of real-world settings, from credit risk assessment to clinical decision support. Rece…
Encrypted accelerated least squares regression
Pedro M. Esperança, Louis J. M. Aslett, Chris C. Holmes
Information that is stored in an encrypted format is, by definition, usually not amenable to statistical analysis or machine learning methods. In this paper we present detailed ana…
Encrypted statistical machine learning: new privacy preserving methods
Louis J. M. Aslett, Pedro M. Esperança, Chris C. Holmes
We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data. The introduction of FHE schemes following Gentry (2009) opens u…
A review of homomorphic encryption and software tools for encrypted statistical machine learning
Louis J. M. Aslett, Pedro M. Esperança, Chris C. Holmes
Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first…