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2020 – today
- 2024
[c4]Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner:
Translation Equivariant Transformer Neural Processes. ICML 2024- [i10]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 abs/2406.08569 (2024) - [i9]Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner:
Translation Equivariant Transformer Neural Processes. CoRR abs/2406.12409 (2024) - [i8]Matthew Ashman, Cristiana Diaconu, Adrian Weller, Wessel P. Bruinsma, Richard E. Turner:
Approximately Equivariant Neural Processes. CoRR abs/2406.13488 (2024) - [i7]Matthew Ashman, Cristiana Diaconu, Adrian Weller, Richard E. Turner:
In-Context In-Context Learning with Transformer Neural Processes. CoRR abs/2406.13493 (2024) - 2023
- [j1]Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela:
Differentially private partitioned variational inference. Trans. Mach. Learn. Res. 2023 (2023) - [c3]Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang:
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. ICLR 2023 - [c2]Vivek Palaniappan, Matthew Ashman, Katherine M. Collins, Juyeon Heo, Adrian Weller, Umang Bhatt:
GeValDi: Generative Validation of Discriminative Models. Tiny Papers @ ICLR 2023 - [i6]Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang:
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. CoRR abs/2303.12703 (2023) - [i5]Matthew Ashman, Tommy Rochussen, Adrian Weller:
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning. CoRR abs/2310.15786 (2023) - 2022
- [i4]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 abs/2202.12275 (2022) - [i3]Mikko A. Heikkilä
, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela:
Differentially private partitioned variational inference. CoRR abs/2209.11595 (2022) - 2021
- [c1]Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch:
Scalable Gaussian Process Variational Autoencoders. AISTATS 2021: 3511-3519 - [i2]Andrei Margeloiu, Matthew Ashman, Umang Bhatt, Yanzhi Chen, Mateja Jamnik, Adrian Weller:
Do Concept Bottleneck Models Learn as Intended? CoRR abs/2105.04289 (2021) - 2020
- [i1]Matthew Ashman, Jonathan So, William Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner:
Sparse Gaussian Process Variational Autoencoders. CoRR abs/2010.10177 (2020)
Coauthor Index
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