Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise J Wyatt, A Leach, SM Schmon, CG Willcocks Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 177 | 2022 |
Capturing label characteristics in VAEs T Joy, SM Schmon, PHS Torr, N Siddharth, T Rainforth International Conference on Learning Representations, 2021 | 53* | 2021 |
Denoising diffusion probabilistic models on so (3) for rotational alignment A Leach, SM Schmon, MT Degiacomi, CG Willcocks ICLR 2022 Workshop on Geometrical and Topological Representation Learning, 2022 | 45 | 2022 |
Large Sample Asymptotics of the Pseudo-Marginal Method SM Schmon, G Deligiannidis, A Doucet, MK Pitt Biometrika 108 (1), 37–51, 2021 | 38 | 2021 |
Neural odes for multi-state survival analysis S Groha, SM Schmon, A Gusev stat 1050, 8, 2020 | 29* | 2020 |
Estimating the density of ethnic minorities and aged people in Berlin: multivariate kernel density estimation applied to sensitive georeferenced administrative data protected … M Groß, U Rendtel, T Schmid, S Schmon, N Tzavidis Journal of the Royal Statistical Society Series A: Statistics in Society 180 …, 2017 | 29 | 2017 |
Black-box Bayesian inference for agent-based models J Dyer, P Cannon, JD Farmer, SM Schmon Journal of Economic Dynamics and Control, 104827, 2024 | 28* | 2024 |
Investigating the impact of model misspecification in neural simulation-based inference P Cannon, D Ward, SM Schmon arXiv preprint arXiv:2209.01845, 2022 | 28* | 2022 |
Generalized posteriors in approximate Bayesian computation SM Schmon, PW Cannon, J Knoblauch Third Symposium on Advances in Approximate Bayesian Inference, 2020 | 26 | 2020 |
Learning Multimodal VAEs through Mutual Supervision T Joy, Y Shi, PHS Torr, T Rainforth, SM Schmon, N Siddharth International Conference on Learning Representations (Spotlight), 2022 | 25 | 2022 |
Robust Neural Posterior Estimation and Statistical Model Criticism D Ward, P Cannon, M Beaumont, M Fasiolo, SM Schmon Neural Information Processing Systems 36, 2022 | 22 | 2022 |
Approximate bayesian computation with path signatures J Dyer, P Cannon, SM Schmon UAI (Spotlight), 2024 | 16 | 2024 |
Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics SM Schmon, P Gagnon Statistics and Computing 32 (2), 2022 | 15 | 2022 |
Calibrating agent-based models to microdata with graph neural networks J Dyer, P Cannon, JD Farmer, SM Schmon arXiv preprint arXiv:2206.07570, 2022 | 14 | 2022 |
Amortised likelihood-free inference for expensive time-series simulators with signatured ratio estimation J Dyer, PW Cannon, SM Schmon International Conference on Artificial Intelligence and Statistics, 11131-11144, 2022 | 8 | 2022 |
Deep signature statistics for likelihood-free time-series models J Dyer, PW Cannon, SM Schmon ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit …, 2021 | 8 | 2021 |
Approximate Bayesian Computation for Panel Data with Signature Maximum Mean Discrepancies J Dyer, J Fitzgerald, B Rieck, SM Schmon NeurIPS 2022 Temporal Graph Learning Workshop, 2022 | 6 | 2022 |
Bernoulli Race Particle Filters SM Schmon, G Deligiannidis, A Doucet International Conference on Artificial Intelligence and Statistics 22, 2350-2358, 2019 | 6 | 2019 |
Calibrating Agent-based Models to Microdata with Graph Neural Networks JD Farmer, J Dyer, P Cannon, S Schmon INET Oxford Working Papers, 2022 | 1 | 2022 |
On Monte Carlo methods for intractable latent variable models S Schmon University of Oxford, 2020 | 1 | 2020 |