Matthew Ashman

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 14

Top Topics

Variational Inference

Top Venues

Tiny Papers @ ICLR

Publications

Matthew Ashman, Cristiana Diaconu, Adrian Weller, Wessel P. Bruinsma, Richard E. Turner
Approximately Equivariant Neural Processes. CoRR (2024)
Ossi Räisä, Stratis Markou, Matthew Ashman, Wessel P. Bruinsma, Marlon Tobaben, Antti Honkela, Richard E. Turner
Noise-Aware Differentially Private Regression via Meta-Learning. CoRR (2024)
Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner
Translation Equivariant Transformer Neural Processes. CoRR (2024)
Matthew Ashman, Cristiana Diaconu, Adrian Weller, Richard E. Turner
In-Context In-Context Learning with Transformer Neural Processes. CoRR (2024)
Matthew Ashman, Tommy Rochussen, Adrian Weller
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning. CoRR (2023)
Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. CoRR (2023)
Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. ICLR (2023)
Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela
Differentially private partitioned variational inference. Trans. Mach. Learn. Res. 2023 (2023)
Vivek Palaniappan, Matthew Ashman, Katherine M. Collins, Juyeon Heo, Adrian Weller, Umang Bhatt
GeValDi: Generative Validation of Discriminative Models. Tiny Papers @ ICLR (2023)
Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela
Differentially private partitioned variational inference. CoRR (2022)
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. CoRR (2022)
Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
Scalable Gaussian Process Variational Autoencoders. AISTATS (2021)
Andrei Margeloiu, Matthew Ashman, Umang Bhatt, Yanzhi Chen, Mateja Jamnik, Adrian Weller
Do Concept Bottleneck Models Learn as Intended? CoRR (2021)
Matthew Ashman, Jonathan So, William Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner
Sparse Gaussian Process Variational Autoencoders. CoRR (2020)