58 citations · 200 across the 12 of their papers we have counts for
13 papers
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…
Ethical considerations of use of hold-out sets in clinical prediction model management
Louis Chislett, Louis JM Aslett, Alisha R Davies +2
Clinical prediction models are statistical or machine learning models used to quantify the risk of a certain health outcome using patient data. These can then inform potential inte…
kalis: A Modern Implementation of the Li & Stephens Model for Local Ancestry Inference in R
Louis J. M. Aslett, Ryan R. Christ
Approximating the recent phylogeny of phased haplotypes at a set of variants along the genome is a core problem in modern population genomics and central to performing genome-w…
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…
Improved Concentration Bounds for Gaussian Quadratic Forms
Robert E. Gallagher, Louis J. M. Aslett, David Steinsaltz +1
For a wide class of monotonic functions , we develop a Chernoff-style concentration inequality for quadratic forms , where $Z_…
Reliability analysis of general phased mission systems with a new survival signature
Xianzhen Huang, Louis J. M. Aslett, Frank P. A. Coolen
It is often difficult for a phased mission system (PMS) to be highly reliable, because this entails achieving high reliability in every phase of operation. Consequently, reliabilit…
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…
Multilevel Monte Carlo for Reliability Theory
Louis J. M. Aslett, Tigran Nagapetyan, Sebastian J. Vollmer
As the size of engineered systems grows, problems in reliability theory can become computationally challenging, often due to the combinatorial growth in the cut sets. In this paper…
Cryptographically secure multiparty evaluation of system reliability
Louis J. M. Aslett
The precise design of a system may be considered a trade secret which should be protected, whilst at the same time component manufacturers are sometimes reluctant to release full t…
Bayesian Nonparametric System Reliability using Sets of Priors
Gero Walter, Louis J. M. Aslett, Frank P. A. Coolen
An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on com…
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…