17 citations · 19 across the 4 of their papers we have counts for
8 papers
Flexible model composition in machine learning and its implementation in MLJ
Anthony D. Blaom, Sebastian J. Vollmer
A graph-based protocol called `learning networks' which combine assorted machine learning models into meta-models is described. Learning networks are shown to overcome several limi…
Foundations of Bayesian Learning from Synthetic Data
Harrison Wilde, Jack Jewson, Sebastian Vollmer +1
There is significant growth and interest in the use of synthetic data as an enabler for machine learning in environments where the release of real data is restricted due to privacy…
Debiasing classifiers: is reality at variance with expectation?
Ashrya Agrawal, Florian Pfisterer, Bernd Bischl +5
We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse r…
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…
A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services
Harrison Wilde, Lucia Lushi Chen, Austin Nguyen +7
Rough sleeping is a chronic problem faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link, a UK-…
MLJ: A Julia package for composable machine learning
Anthony D. Blaom, Franz Kiraly, Thibaut Lienart +3
MLJ (Machine Learing in Julia) is an open source software package providing a common interface for interacting with machine learning models written in Julia and other languages. It…