7 citations · 11 across the 6 of their papers we have counts for
8 papers
Prespecification of Structure for Optimizing Data Collection and Research Transparency by Leveraging Conditional Independencies
Matthew J. Vowels
Data collection and research methodology represents a critical part of the research pipeline. On the one hand, it is important that we collect data in a way that maximises the vali…
Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables
Matthew J. Vowels
Machine Learning explainability techniques have been proposed as a means of `explaining' or interrogating a model in order to understand why a particular decision or prediction has…
Improving Robot Localisation by Ignoring Visual Distraction
Oscar Mendez, Matthew Vowels, Richard Bowden
Attention is an important component of modern deep learning. However, less emphasis has been put on its inverse: ignoring distraction. Our daily lives require us to explicitly avoi…
BERT meets LIWC: Exploring State-of-the-Art Language Models for Predicting Communication Behavior in Couples' Conflict Interactions
Jacopo Biggiogera, George Boateng, Peter Hilpert +5
Many processes in psychology are complex, such as dyadic interactions between two interacting partners (e.g. patient-therapist, intimate relationship partners). Nevertheless, many…
Shadow-Mapping for Unsupervised Neural Causal Discovery
Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
An important goal across most scientific fields is the discovery of causal structures underling a set of observations. Unfortunately, causal discovery methods which are based on co…
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery
Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
Causal reasoning is a crucial part of science and human intelligence. In order to discover causal relationships from data, we need structure discovery methods. We provide a review…