Sebastian M Schmon
Sebastian M Schmon
Assistant Professor in Statistics, Durham | Research Scientist, Improbable
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Zitiert von
Zitiert von
Large Sample Asymptotics of the Pseudo-Marginal Method
SM Schmon, G Deligiannidis, A Doucet, MK Pitt
Biometrika 108 (1), 37–51, 2021
Capturing label characteristics in VAEs
T Joy, SM Schmon, PHS Torr, N Siddharth, T Rainforth
International Conference on Learning Representations, 2021
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 …, 2017
Neural odes for multi-state survival analysis
S Groha, SM Schmon, A Gusev
stat 1050, 8, 2020
Generalized posteriors in approximate bayesian computation
SM Schmon, PW Cannon, J Knoblauch
arXiv preprint arXiv:2011.08644, 2020
Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics
SM Schmon, P Gagnon
Statistics and Computing 32 (2), 2022
Bernoulli Race Particle Filters
SM Schmon, G Deligiannidis, A Doucet
International Conference on Artificial Intelligence and Statistics 22, 2350-2358, 2019
Approximate Bayesian Computation with Path Signatures
J Dyer, P Cannon, SM Schmon
arXiv preprint arXiv:2106.12555, 2021
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
Black-box Bayesian inference for economic agent-based models
J Dyer, P Cannon, JD Farmer, S Schmon
arXiv preprint arXiv:2202.00625, 2022
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
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
On Monte Carlo methods for intractable latent variable models
S Schmon
University of Oxford, 2020
Calibrating Agent-based Models to Microdata with Graph Neural Networks
J Dyer, P Cannon, JD Farmer, SM Schmon
arXiv preprint arXiv:2206.07570, 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
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
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks
J Fitzsimons, SM Schmon, S Roberts
4th Neurips workshop on Bayesian Deep Learning 2019, 2019
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