Ashman, Matthew

10 publications

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

ICML 2025 Gridded Transformer Neural Processes for Spatio-Temporal Data Matthew Ashman, Cristiana Diaconu, Eric Langezaal, Adrian Weller, Richard E Turner

TMLR 2025 Tighter Sparse Variational Gaussian Processes Thang D Bui, Matthew Ashman, Richard E. Turner

NeurIPS 2024 Approximately Equivariant Neural Processes Matthew Ashman, Cristiana Diaconu, Adrian Weller, Wessel 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

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

ICLR 2023 Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang

TMLR 2023 Differentially Private Partitioned Variational Inference Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E Turner, Antti Honkela

ICLRW 2023 GeValDi: Generative Validation of Discriminative Models Vivek Palaniappan, Matthew Ashman, Katherine M. Collins, Juyeon Heo, Adrian Weller, Umang Bhatt

NeurIPSW 2022 Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang