7 citations · 17 across the 21 of their papers we have counts for
26 papers
Kurate: Scalable Scientific Quality Analysis
Matthew J. Vowels, Jamie Cummins
Scientific search systems can find papers that are relevant to a question, but they generally do not assess the quality of the evidence that those papers provide. We present Kurate…
K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations
Laura M. Vowels, Matthew J. Vowels, Shivali Sharma +9
People increasingly use large language models (LLMs) for mental health support, yet their safety in evolving, high-risk conversations remains poorly characterised. We developed K-B…
Quantifying the Relationship Between Clinical Safety and Environmental Impact in Therapeutic LLMs
Alireza A. Safaei, Laura M. Vowels, Matthew J. Vowels +2
The deployment of large language models (LLMs) in mental health contexts raises questions about the relationship between clinical safety and environmental cost. In this paper, we e…
Sequences as Nodes for Contrastive Multimodal Graph Recommendation
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
To tackle cold-start and data sparsity issues in recommender systems, numerous multimodal, sequential, and contrastive techniques have been proposed. While these augmentations can…
Multimodal Enhancement of Sequential Recommendation
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
We propose a novel recommender framework, MuSTRec (Multimodal and Sequential Transformer-based Recommendation), that unifies multimodal and sequential recommendation paradigms. MuS…
DSL: Understanding and Improving Softmax Recommender Systems with Competition-Aware Scaling
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
Softmax Loss (SL) is being increasingly adopted for recommender systems (RS) as it has demonstrated better performance, robustness and fairness. Yet in implicit-feedback, a single…
Differential Adjusted Parity for Learning Fair Representations
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
The development of fair and unbiased machine learning models remains an ongoing objective for researchers in the field of artificial intelligence. We introduce the Differential Adj…
CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers for Causally Constrained Predictions
Matthew J. Vowels, Mathieu Rochat, Sina Akbari
Artificial Neural Networks (ANNs), including fully-connected networks and transformers, are highly flexible and powerful function approximators, widely applied in fields like compu…
Modelling the Distribution of Human Motion for Sign Language Assessment
Oliver Cory, Ozge Mercanoglu Sincan, Matthew Vowels +7
Sign Language Assessment (SLA) tools are useful to aid in language learning and are underdeveloped. Previous work has focused on isolated signs or comparison against a single refer…
psifx -- Psychological and Social Interactions Feature Extraction Package
Guillaume Rochette, Mathieu Rochat, Nizar Michaud +1
psifx is a plug-and-play multi-modal feature extraction toolkit, aiming to facilitate and democratize the use of state-of-the-art machine learning techniques for human sciences res…
SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling
Matthew J. Vowels
Causal inference is a crucial goal of science, enabling researchers to arrive at meaningful conclusions regarding the predictions of hypothetical interventions using observational…
Typical Yet Unlikely and Normally Abnormal: The Intuition Behind High-Dimensional Statistics
Matthew J. Vowels
Normality, in the colloquial sense, has historically been considered an aspirational trait, synonymous with ideality. The arithmetic average and, by extension, statistics including…
Causal Effect Identification in Uncertain Causal Networks
Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian +3
Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these…
A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists
Matthew J. Vowels
Causality is a fundamental part of the scientific endeavour to understand the world. Unfortunately, causality is still taboo in much of psychology and social science. Motivated by…
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…
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics
Matthew J. Vowels, Sina Akbari, Necati Cihan Camgoz +1
Parameter estimation in empirical fields is usually undertaken using parametric models, and such models readily facilitate statistical inference. Unfortunately, they are unlikely t…
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…
VDSM: Unsupervised Video Disentanglement with State-Space Modeling and Deep Mixtures of Experts
Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
Disentangled representations support a range of downstream tasks including causal reasoning, generative modeling, and fair machine learning. Unfortunately, disentanglement has been…
Targeted VAE: Variational and Targeted Learning for Causal Inference
Matthew James Vowels, Necati Cihan Camgoz, Richard Bowden
Undertaking causal inference with observational data is incredibly useful across a wide range of tasks including the development of medical treatments, advertisements and marketing…
Misspecification and Unreliable Interpretations in Psychology and Social Science
Matthew J. Vowels
The replicability crisis has drawn attention to numerous weaknesses in psychology and social science research practice. In this work we focus on three issues that cannot be address…