NestedVAE: Isolating Common Factors via Weak Supervision. - Matthew J. Vowels; Necati Cihan Camgöz; Richard Bowden (CVPR, 2020)

Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement. - Matthew J. Vowels; Necati Cihan Camgöz; Richard Bowden (FG, 2020)

Targeted VAE: Structured Inference and Targeted Learning for Causal Parameter Estimation. - Matthew James Vowels; Necati Cihan Camgöz; Richard Bowden (CoRR, 2020)

VDSM: Unsupervised Video Disentanglement With State-Space Modeling and Deep Mixtures of Experts. - Matthew J. Vowels; Necati Cihan Camgöz; Richard Bowden (CVPR, 2021)

Shadow-Mapping for Unsupervised Neural Causal Discovery. - Matthew J. Vowels; Necati Cihan Camgöz; Richard Bowden (CVPR Workshops, 2021)

BERT meets LIWC: Exploring State-of-the-Art Language Models for Predicting Communication Behavior in Couples' Conflict Interactions. - Jacopo Biggiogera; George Boateng; Peter Hilpert; Matthew J. Vowels; Guy Bodenmann; Mona Neysari; Fridtjof Nussbeck; Tobias Kowatsch (ICMI Companion, 2021)

Improving Robot Localisation by Ignoring Visual Distraction. - Oscar Mendez Maldonado; Matthew J. Vowels; Richard Bowden (IROS, 2021)

Targeted VAE: Variational and Targeted Learning for Causal Inference. - Matthew J. Vowels; Necati Cihan Camgöz; Richard Bowden (SMDS, 2021)

A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics. - Matthew J. Vowels; Sina Akbari; Jalal Etesami; Necati Cihan Camgöz; Richard Bowden (CoRR, 2022)

Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables. - Matthew J. Vowels (CoRR, 2022)

Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect. - Sina Akbari; Fateme Jamshidi; Ehsan Mokhtarian; Matthew J. Vowels; Jalal Etesami; Negar Kiyavash (CoRR, 2022)

Causal Effect Identification in Uncertain Causal Networks. - Sina Akbari; Fateme Jamshidi; Ehsan Mokhtarian; Matthew J. Vowels; Jalal Etesami; Negar Kiyavash (NeurIPS, 2023)

SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling. - Matthew J. Vowels (CoRR, 2023)

D'ya Like DAGs? A Survey on Structure Learning and Causal Discovery. - Matthew J. Vowels; Necati Cihan Camgöz; Richard Bowden (ACM Comput. Surv., 2023)

A Free Lunch with Influence Functions? An Empirical Evaluation of Influence Functions for Average Treatment Effect Estimation. - Matthew James Vowels; Sina Akbari; Necati Cihan Camgöz; Richard Bowden (Trans. Mach. Learn. Res., 2023)

Modelling the Distribution of Human Motion for Sign Language Assessment. - Oliver Cory; Ozge Mercanoglu Sincan; Matthew J. Vowels; Alessia Battisti; Franz Holzknecht; Katja Tissi; Sandra Sidler-Miserez; Tobias Haug; Sarah Ebling; Richard Bowden (ECCV Workshops (12), 2024)

psifx - Psychological and Social Interactions Feature Extraction Package. - Guillaume Rochette; Matthew J. Vowels (CoRR, 2024)

CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers and Fully-Connected Neural Networks for Causally Constrained Predictions. - Matthew J. Vowels; Mathieu Rochat; Sina Akbari (CoRR, 2024)

Deepfake Detection in Super-Recognizers and Police Officers. - Meike Ramon; Matthew J. Vowels; Matthew Groh (IEEE Secur. Priv., 2024)

Differential Adjusted Parity for Learning Fair Representations. - Bucher Sahyouni; Matthew J. Vowels; Liqun Chen; Simon Hadfield (CoRR, 2025)