5 citations · 11 across the 6 of their papers we have counts for
7 papers
MOODE: An R Package for Multi-Objective Optimal Design of Experiments
Vasiliki Koutra, Olga Egorova, Steven G. Gilmour +1
We describe the R package MOODE and demonstrate its use to find multi-objective optimal experimental designs. Multi-Objective Optimal Design of Experiments (MOODE) targets the expe…
Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19
Davide Pigoli, Kieran Baker, Jobie Budd +19
Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on voca…
Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers
Harry Coppock, George Nicholson, Ivan Kiskin +22
Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, w…
A large-scale and PCR-referenced vocal audio dataset for COVID-19
Jobie Budd, Kieran Baker, Emma Karoune +23
The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory sympt…
Designs with complex blocking structures and network effects for agricultural field experiments
Vasiliki Koutra, Steven G. Gilmour, Ben M. Parker +1
We propose a novel model-based approach for constructing optimal designs with complex blocking structures and network effects, for application in agricultural field experiments. Th…
Optimal block designs for experiments on networks
Vasiliki Koutra, Steven G. Gilmour, Ben M. Parker
We propose a method for constructing optimal block designs for experiments on networks. The response model for a given network interference structure extends the linear network eff…
A graph-theoretic framework for algorithmic design of experiments
Ben M. Parker, Steven G Gilmour, Vasiliki Koutra
In this paper, we demonstrate that considering experiments in a graph-theoretic manner allows us to exploit automorphisms of the graph to reduce the number of evaluations of candid…