Sparse Gaussian Process Variational Autoencoders. - Matthew Ashman; Jonathan So; William Tebbutt; Vincent Fortuin; Michael Pearce; Richard E. Turner (CoRR, 2020)

Scalable Gaussian Process Variational Autoencoders. - Metod Jazbec; Matthew Ashman; Vincent Fortuin; Michael Pearce; Stephan Mandt; Gunnar Rätsch (AISTATS, 2021)

Do Concept Bottleneck Models Learn as Intended? - Andrei Margeloiu; Matthew Ashman; Umang Bhatt; Yanzhi Chen; Mateja Jamnik; Adrian Weller (CoRR, 2021)

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)

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. - Matthew Ashman; Chao Ma 019; Agrin Hilmkil; Joel Jennings; Cheng Zhang 005 (ICLR, 2023)

GeValDi: Generative Validation of Discriminative Models. - Vivek Palaniappan; Matthew Ashman; Katherine M. Collins; Juyeon Heo; Adrian Weller; Umang Bhatt (Tiny Papers @ ICLR, 2023)

Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning. - Matthew Ashman; Tommy Rochussen; Adrian Weller (CoRR, 2023)

Differentially private partitioned variational inference. - Mikko A. Heikkilä; Matthew Ashman; Siddharth Swaroop; Richard E. Turner; Antti Honkela (Trans. Mach. Learn. Res., 2023)

In-Context In-Context Learning with Transformer Neural Processes. - Matthew Ashman; Cristiana Diaconu; Adrian Weller; Richard E. Turner (AABI, 2024)

Translation Equivariant Transformer Neural Processes. - Matthew Ashman; Cristiana Diaconu; Junhyuck Kim; Lakee Sivaraya; Stratis Markou; James Requeima; Wessel P. Bruinsma; Richard E. Turner (ICML, 2024)

Approximately Equivariant Neural Processes. - Matthew Ashman; Cristiana Diaconu; Adrian Weller; Wessel P. Bruinsma; Richard E. Turner (NeurIPS, 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 (NeurIPS, 2024)

Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data. - Matthew Ashman; Cristiana Diaconu; Eric Langezaal; Adrian Weller; Richard E. Turner (CoRR, 2024)

A Meta-Learning Approach to Bayesian Causal Discovery. - Anish Dhir; Matthew Ashman; James Requeima; Mark van der Wilk (ICLR, 2025)

Tighter sparse variational Gaussian processes. - Thang D. Bui; Matthew Ashman; Richard E. Turner (Trans. Mach. Learn. Res., 2025)