Matthew J. Vowels

Orcid: 0000-0002-8811-1156

According to our database1, Matthew J. Vowels authored at least 17 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
Deepfake Detection in Super-Recognizers and Police Officers.
IEEE Secur. Priv., 2024

psifx - Psychological and Social Interactions Feature Extraction Package.
CoRR, 2024

2023
A Free Lunch with Influence Functions? An Empirical Evaluation of Influence Functions for Average Treatment Effect Estimation.
Trans. Mach. Learn. Res., 2023

D'ya Like DAGs? A Survey on Structure Learning and Causal Discovery.
ACM Comput. Surv., 2023

SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling.
CoRR, 2023

Causal Effect Identification in Uncertain Causal Networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect.
CoRR, 2022

Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables.
CoRR, 2022

A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics.
CoRR, 2022

2021
Targeted VAE: Variational and Targeted Learning for Causal Inference.
Proceedings of the IEEE International Conference on Smart Data Services, 2021

Improving Robot Localisation by Ignoring Visual Distraction.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

BERT meets LIWC: Exploring State-of-the-Art Language Models for Predicting Communication Behavior in Couples' Conflict Interactions.
Proceedings of the ICMI '21 Companion: Companion Publication of the 2021 International Conference on Multimodal Interaction, Montreal, QC, Canada, October 18, 2021

Shadow-Mapping for Unsupervised Neural Causal Discovery.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

VDSM: Unsupervised Video Disentanglement With State-Space Modeling and Deep Mixtures of Experts.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Targeted VAE: Structured Inference and Targeted Learning for Causal Parameter Estimation.
CoRR, 2020

Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement.
Proceedings of the 15th IEEE International Conference on Automatic Face and Gesture Recognition, 2020

NestedVAE: Isolating Common Factors via Weak Supervision.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020


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