Research

My research interests are motivated by the need for the effective and efficient deployment of probabilistic machine learning techniques to improve decision making and prediction in the presence of uncertainty.

Publications

1. Matthew Ashman*, Tommy Rochussen*, Adrian Weller
   Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning
   5th Symposium on Advances in Approximate Bayesian Inference 2023
   Paper

2. Matthew Ashman*, Chao Ma*, Agrin Hilmkil, Joel Jennings, Cheng Zhang
   Causal Reasoning in the Presence of Latent Confounders
   International Conference on Learning Representations (ICLR) 2023
   Paper

3. Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela
   Differentially Private Partitioned Variational Inference
   Transactions of Machine Learning Research (TMLR) 2023
   Paper

4. Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Efstratios Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner
   Partitioned Variational Inference: A Framework for Probabilistic Federated Learning Submitted to JMLR
   Paper

5. Andrei Margeloiu*, Matthew Ashman*, Umang Bhatt*, Yanzhi Chen, Mateja Jamnik, Adrian Weller
   Do Concept Bottleneck Models Learn As Intended?
   ICLR Workshop on Responsible AI, 2021.
   Paper

6. Metod Jazbec, Matthew Ashman, Vincent Fortuin, Micheal Pearce, Stephen Mandt, Gunnar Rätsch
   Scalable Gaussian Process Variational Autoencoders
   International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
   Paper

7. Matthew Ashman, Jonathan So, Will Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner
   Sparse Gaussian Process Variational Autoencoders
   arxiv, 2021
   Paper