Matthew Ashman
Matthew Ashman
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Do concept bottleneck models learn as intended?
A Margeloiu, M Ashman, U Bhatt, Y Chen, M Jamnik, A Weller
arXiv preprint arXiv:2105.04289, 2021
Scalable gaussian process variational autoencoders
M Jazbec, M Ashman, V Fortuin, M Pearce, S Mandt, G Rätsch
arXiv preprint arXiv:2010.13472, 2020
Sparse Gaussian Process Variational Autoencoders
M Ashman, J So, W Tebbutt, V Fortuin, M Pearce, RE Turner
arXiv preprint arXiv:2010.10177, 2020
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
M Ashman, TD Bui, CV Nguyen, E Markou, A Weller, S Swaroop, ...
arXiv preprint arXiv:2202.12275, 2022
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
M Ashman, C Ma, A Hilmkil, J Jennings, C Zhang
ICLR 2023, 2023
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning
M Ashman, T Rochussen, A Weller
arXiv preprint arXiv:2310.15786, 2023
GeValDi: Generative Validation of Discriminative Models
V Palaniappan, M Ashman, KM Collins, J Heo, A Weller, U Bhatt
ICLR 2023 Workshop on Pitfalls of limited data and computation for …, 2023
Differentially Private Partitioned Variational Inference
MA Heikkilä, M Ashman, S Swaroop, RE Turner, A Honkela
TMLR, 2023
Spatio-Temporal Variational Autoencoders
MC Ashman
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