| Compositional uncertainty in deep Gaussian processes I Ustyuzhaninov, I Kazlauskaite, M Kaiser, E Bodin, NDF Campbell, CH Ek The Conference on Uncertainty in Artificial Intelligence (UAI 2020), 2019 | 23 | 2019 |
| Modulating Surrogates for Bayesian Optimization E Bodin, M Kaiser, I Kazlauskaite, Z Dai, NDF Campbell, CH Ek International Conference on Machine Learning (ICML 2020), 2019 | 16 | 2019 |
| Latent gaussian process regression E Bodin, NDF Campbell, CH Ek arXiv preprint arXiv:1707.05534, 2017 | 15 | 2017 |
| Nonparametric Inference for Auto-Encoding Variational Bayes E Bodin, I Malik, CH Ek, NDF Campbell Advances in Approximate Bayesian Inference @ NeurIPS 2017, 2017 | 14 | 2017 |
| Black-box density function estimation using recursive partitioning E Bodin, Z Dai, NDF Campbell, CH Ek International Conference on Machine Learning (ICML 2021), 2020 | 5 | 2020 |
| Gaussian process deep belief networks: A smooth generative model of shape with uncertainty propagation A Di Martino, E Bodin, CH Ek, NDF Campbell Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth …, 2019 | 3 | 2019 |
| Linear combinations of Gaussian latents in generative models: interpolation and beyond E Bodin, CH Ek, H Moss arXiv preprint arXiv:2408.08558, 2024 | | 2024 |
| Linear combinations of latents in diffusion models: interpolation and beyond E Bodin, H Moss, CH Ek arXiv e-prints, arXiv: 2408.08558, 2024 | | 2024 |
| Making Differentiable Architecture Search less local E Bodin, F Tomasi, Z Dai Workshop on Neural Architecture Search (ICLR 2021), 2021 | | 2021 |
| Bayesian inference by active sampling E Bodin University of Bristol, 2021 | | 2021 |
| Black-box density function estimation using recursive partitioning Supplementary material E Bodin, Z Dai, NDF Campbell, CH Ek | | |
| Density function estimation using ergodic recursion E Bodin, Z Dai, NDF Campbell, CH Ek Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), 0 | | |
