116 citations · 415 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
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…
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
Leonard Hasenclever, Stefan Webb, Thibaut Lienart +4
This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to e…