Sebastian M Schmon
Sebastian M Schmon
Other namesSebastian Schmon
Senior Staff Machine Learning Engineer at Altos Labs
Verified email at - Homepage
Cited by
Cited by
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
Capturing label characteristics in VAEs
T Joy, SM Schmon, PHS Torr, N Siddharth, T Rainforth
International Conference on Learning Representations, 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
Large Sample Asymptotics of the Pseudo-Marginal Method
SM Schmon, G Deligiannidis, A Doucet, MK Pitt
Biometrika 108 (1), 37–51, 2021
Neural odes for multi-state survival analysis
S Groha, SM Schmon, A Gusev
stat 1050, 8, 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
Black-box Bayesian inference for agent-based models
J Dyer, P Cannon, JD Farmer, SM Schmon
Journal of Economic Dynamics and Control, 104827, 2024
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
Investigating the impact of model misspecification in neural simulation-based inference
P Cannon, D Ward, SM Schmon
arXiv preprint arXiv:2209.01845, 2022
Generalized posteriors in approximate Bayesian computation
SM Schmon, PW Cannon, J Knoblauch
Third Symposium on Advances in Approximate Bayesian Inference, 2020
Robust Neural Posterior Estimation and Statistical Model Criticism
D Ward, P Cannon, M Beaumont, M Fasiolo, SM Schmon
Neural Information Processing Systems 36, 2022
Approximate bayesian computation with path signatures
J Dyer, P Cannon, SM Schmon
arXiv preprint arXiv:2106.12555, 2021
Calibrating agent-based models to microdata with graph neural networks
J Dyer, P Cannon, JD Farmer, SM Schmon
arXiv preprint arXiv:2206.07570, 2022
Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics
SM Schmon, P Gagnon
Statistics and Computing 32 (2), 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
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
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
Bernoulli Race Particle Filters
SM Schmon, G Deligiannidis, A Doucet
International Conference on Artificial Intelligence and Statistics 22, 2350-2358, 2019
On Monte Carlo methods for intractable latent variable models
S Schmon
University of Oxford, 2020
SurviVAEl: Variational Autoencoders for Clustering Time Series
S Groha, A Gusev, SM Schmon
NeurIPS 2022 Workshop on Learning from Time Series for Health, 2022
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