Matthew Ashman

According to our database, Matthew Ashman authored at least 15 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Tighter sparse variational Gaussian processes.

Thang D. Bui, Matthew Ashman, Richard E. Turner

Trans. Mach. Learn. Res., 2025

A Meta-Learning Approach to Bayesian Causal Discovery.

Anish Dhir, Matthew Ashman, James Requeima, Mark van der Wilk

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data.

Matthew Ashman, Cristiana Diaconu, Eric Langezaal, Adrian Weller, Richard E. Turner

CoRR, 2024

Noise-Aware Differentially Private Regression via Meta-Learning.

Ossi Räisä, Stratis Markou, Matthew Ashman, Wessel P. Bruinsma, Marlon Tobaben, Antti Honkela, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Approximately Equivariant Neural Processes.

Matthew Ashman, Cristiana Diaconu, Adrian Weller, Wessel P. Bruinsma, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Translation Equivariant Transformer Neural Processes.

Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner

Proceedings of the Forty-first International Conference on Machine Learning, 2024

In-Context In-Context Learning with Transformer Neural Processes.

Matthew Ashman, Cristiana Diaconu, Adrian Weller, Richard E. Turner

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2024

2023

Differentially private partitioned variational inference.

Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela

Trans. Mach. Learn. Res., 2023

Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning.

Matthew Ashman, Tommy Rochussen, Adrian Weller

CoRR, 2023

GeValDi: Generative Validation of Discriminative Models.

Vivek Palaniappan, Matthew Ashman, Katherine M. Collins, Juyeon Heo, Adrian Weller, Umang Bhatt

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning.

Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Partitioned Variational Inference: A Framework for Probabilistic Federated Learning.

Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Efstratios Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner

CoRR, 2022

2021

Do Concept Bottleneck Models Learn as Intended?

Andrei Margeloiu, Matthew Ashman, Umang Bhatt, Yanzhi Chen, Mateja Jamnik, Adrian Weller

CoRR, 2021

Scalable Gaussian Process Variational Autoencoders.

Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Sparse Gaussian Process Variational Autoencoders.

Matthew Ashman, Jonathan So, William Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner

CoRR, 2020