publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

2024

  1. JEDC
    Black-box Bayesian inference for agent-based models
    Joel Dyer , Patrick Cannon , J Doyne Farmer , and Sebastian Schmon
    Journal of Economic Dynamics and Control, 2024

2022

  1. CVPR
    Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise
    Julian Wyatt , Adam Leach , Sebastian Schmon, and Chris G Willcocks
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022
  2. ICLR
    Denoising diffusion probabilistic models on so (3) for rotational alignment
    Adam Leach , Sebastian Schmon, Matteo T Degiacomi , and Chris G Willcocks
    In ICLR 2022 Workshop on Geometrical and Topological Representation Learning , 2022
  3. ICLR
    Learning Multimodal VAEs through Mutual Supervision
    Tom Joy , Yuge Shi , Philip Torr , Tom Rainforth , Sebastian Schmon, and Siddharth N
    In International Conference on Learning Representations , 2022
  4. Investigating the impact of model misspecification in neural simulation-based inference
    Patrick Cannon , Daniel Ward , and Sebastian Schmon
    arXiv preprint arXiv:2209.01845, 2022
  5. NeuRIPS
    Robust neural posterior estimation and statistical model criticism
    Daniel Ward , Patrick Cannon , Mark Beaumont , Matteo Fasiolo , and Sebastian Schmon
    Advances in Neural Information Processing Systems, 2022
  6. Calibrating agent-based models to microdata with graph neural networks
    Joel Dyer , Patrick Cannon , J Doyne Farmer , and Sebastian Schmon
    arXiv preprint arXiv:2206.07570, 2022
  7. Stats & Computing
    Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics
    Sebastian Schmon, and Philippe Gagnon
    Statistics and Computing, 2022
  8. AISTATS
    Amortised likelihood-free inference for expensive time-series simulators with signatured ratio estimation
    Joel Dyer , Patrick W Cannon , and Sebastian Schmon
    In International Conference on Artificial Intelligence and Statistics , 2022
  9. Approximate bayesian computation for panel data with signature maximum mean discrepancies
    Joel Dyer , John Fitzgerald , Bastian Rieck , and Sebastian Schmon
    In NeurIPS 2022 Temporal Graph Learning Workshop , 2022

2021

  1. ICLR
    Capturing Label Characteristics in {VAE}s
    Tom Joy , Sebastian Schmon, Philip Torr , Siddharth N , and Tom Rainforth
    In International Conference on Learning Representations , 2021
  2. Biometrika
    Large-sample asymptotics of the pseudo-marginal method
    Sebastian Schmon, George Deligiannidis , Arnaud Doucet , and Michael K Pitt
    Biometrika, 2021
  3. Approximate bayesian computation with path signatures
    Joel Dyer , Patrick Cannon , and Sebastian Schmon
    arXiv preprint arXiv:2106.12555, 2021
  4. Deep Signature Statistics for Likelihood-free Time-series Models
    Joel Dyer , Patrick W Cannon , and Sebastian Schmon
    In ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models , 2021

2020

  1. Neural odes for multi-state survival analysis
    Stefan Groha , Sebastian Schmon, and Alexander Gusev
    stat, 2020
  2. Generalized posteriors in approximate Bayesian computation
    Sebastian Schmon, Patrick W Cannon , and Jeremias Knoblauch
    arXiv preprint arXiv:2011.08644, 2020
  3. On Monte Carlo methods for intractable latent variable models
    Sebastian Schmon
    University of Oxford , 2020

2019

  1. Bernoulli race particle filters
    Sebastian Schmon, Arnaud Doucet , and George Deligiannidis
    In The 22nd International Conference on Artificial Intelligence and Statistics , 2019

2017

  1. JRSS-A
    Estimating the density of ethnic minorities and aged people in Berlin: multivariate kernel density estimation applied to sensitive georeferenced administrative data protected via measurement error
    Marcus Groß , Ulrich Rendtel , Timo Schmid , Sebastian Schmon, and Nikos Tzavidis
    Journal of the Royal Statistical Society Series A: Statistics in Society, 2017