Suivre
Matthew Ashman
Matthew Ashman
PhD Student, University of Cambridge
Adresse e-mail validée de cam.ac.uk - Page d'accueil
Titre
Citée par
Citée par
Année
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
932021
Scalable gaussian process variational autoencoders
M Jazbec, M Ashman, V Fortuin, M Pearce, S Mandt, G Rätsch
arXiv preprint arXiv:2010.13472, 2020
302020
Sparse Gaussian Process Variational Autoencoders
M Ashman, J So, W Tebbutt, V Fortuin, M Pearce, RE Turner
arXiv preprint arXiv:2010.10177, 2020
262020
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
142023
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
132022
Translation Equivariant Transformer Neural Processes
M Ashman, C Diaconu, J Kim, L Sivaraya, S Markou, J Requeima, ...
Forty-first International Conference on Machine Learning, 2024
22024
In-Context In-Context Learning with Transformer Neural Processes
M Ashman, C Diaconu, A Weller, RE Turner
Sixth Symposium on Advances in Approximate Bayesian Inference-Archival Track, 2024
22024
Differentially Private Partitioned Variational Inference
MA Heikkilä, M Ashman, S Swaroop, RE Turner, A Honkela
TMLR, 2023
12023
Tighter sparse variational Gaussian processes
TD Bui, M Ashman, RE Turner
arXiv preprint arXiv:2502.04750, 2025
2025
A Meta-Learning Approach to Bayesian Causal Discovery
A Dhir, M Ashman, J Requeima, M van der Wilk
arXiv preprint arXiv:2412.16577, 2024
2024
Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data
M Ashman, C Diaconu, E Langezaal, A Weller, RE Turner
arXiv preprint arXiv:2410.06731, 2024
2024
Approximately Equivariant Neural Processes
M Ashman, C Diaconu, A Weller, W Bruinsma, RE Turner
arXiv preprint arXiv:2406.13488, 2024
2024
Noise-Aware Differentially Private Regression via Meta-Learning
O Räisä, S Markou, M Ashman, WP Bruinsma, M Tobaben, A Honkela, ...
arXiv preprint arXiv:2406.08569, 2024
2024
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning
M Ashman, T Rochussen, A Weller
arXiv preprint arXiv:2310.15786, 2023
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
2023
Spatio-Temporal Variational Autoencoders
MC Ashman
2020
Le système ne peut pas réaliser cette opération maintenant. Veuillez réessayer plus tard.
Articles 1–16