
Sebastian Schmon,
DPhil (Oxon)
Welcome to my webpage. I am a Research Scientist in the Complexity Research team at Improbable. My research interests lie at the intersection of statistics, machine learning and probability theory. In particular, I am interested in constructing and analysing practical algorithms and simulation techniques for complex statistical or machine learning models such as intractable Bayesian models or large scale simulation engines. A list of publications can be found below. I obtained my DPhil (Oxford parlance for PhD) at the Department of Statistics of the University of Oxford under the supervision of Arnaud Doucet and George Deligiannidis.
As a consultant with several years of experience I have supported start-ups as well as large multinational companies to improve their analytical capabilites.
Publications and Preprints
Chapturing Label Characteristics in VAEs
with Tom Joy, Philipp Torr, Siddharth Narayanaswamy and Tom Rainforth
International Conference on Learning Representations, ICLR 2021
@inproceedings{
joy2021capturing,
title={Capturing Label Characteristics in {\{}VAE{\}}s},
author={Tom Joy and Sebastian Schmon and Philip Torr and Siddharth N and Tom Rainforth},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=wQRlSUZ5V7B}
}
Generalized Posteriors in Approximate Bayesian Computation
with Patrick W Cannon and Jeremias Knoblauch
The 3rd Symposium on Advances in Approximate Bayesian Inference, AABI 2021
@Article{gbiabc,
author = {Schmon, Sebastian M and Patrick W Cannon and Jeremias Knoblauch},
title = {Generalized Posteriors in Approximate Bayesian Computation},
journal = {https://arxiv.org/abs/2011.08644},
year = {2020},
}
Neural ODEs for Multi-state Survival Analysis
with Stefan Groha and Alexander Gusev
arxiv.org/abs/2006.04893
@Article{Groha2020,
author = {Stefan Groha, Sebastian M Schmon and Alexander Gusev},
title = {Neural ODEs for Multi-state Survival Analysis},
journal = {https://arxiv.org/abs/2006.04893},
year = {2020},
}
Large Sample Asymptotics of the Pseudo-Marginal Method
with Arnaud Doucet, George Deligiannidis and Mike Pitt
Biometrika, 2020 (forthcoming)
@Article{SchmonDeligiannidisDoucet2018a,
author = {Sebastian M Schmon and George Deligiannidis and Arnaud Doucet and Michael K Pitt},
title = {Large Sample Asymptotics of the Pseudo-Marginal Algorithm},
journal = {https://arxiv.org/abs/1806.10060},
year = {2018},
}
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks
with Jack Fitzsimons and Stephen Roberts
4th Neurips workshop on Bayesian Deep Learning 2019
@Article{impprior,
author = {Jack K Fitzsimons and Sebastian M Schmon and Stephen J Roberts},
title = {Implicit Priors for Knowledge Sharing in Bayesian Neural Networks},
journal = {4th Neurips workshop on Bayesian Deep Learning},
year = {2019},
}
Bernoulli Race Particle Filters
with Arnaud Doucet, George Deligiannidis
The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019
@Article{bernrace,
author = {Sebastian M Schmon and George Deligiannidis and Arnaud Doucet},
title = {Bernoulli Race Particle Filters},
journal = {AISTATS},
year = {2019},
}
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
with Marcus Groß, Ulrich Rendtel, Timo Schmid and Nikos Tzavidis
Journal of the Royal Statistical Society: Series A (Statistics in Society) 180.1 (2017): 161-183
@article{gross2017,
title={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},
author={Gro{\ss}, Marcus and Rendtel, Ulrich and Schmid, Timo and Schmon, Sebastian and Tzavidis, Nikos},
journal={Journal of the Royal Statistical Society: Series A (Statistics in Society)},
volume={180},
number={1},
pages={161--183},
year={2017},
publisher={Wiley Online Library}
}
Talks & Presentations
Neural ODEs for Multi-state Survival Analysis
Neurips Workshop on Machine Learning 4 Healthcare, ML4h 2020
Bernoulli Race Particle Filters
AISTATS 2019
I presented this work at AISTATS 2019 in Okinawa (poster) and Babylon Health in London (slides).
Large Sample Asymptotics of the Pseudo-Marginal Method
ISBA 2018, BayesComp 2018
Past Teaching
University of Oxford
- Applied Probability, 3rd year Mathematics (Michaelmas 2015)
- Advanced Simulation, 4th year Mathematics and MSc Statistics (Hilary 2016, 2017, 2018)
- Graphical Models, 4th year Mathematics and MSc Statistics (Michaelmas 2016, 2017, 2018)
- Prelims: Probability, 1st year Mathematics (Michaelmas 2016)
- Part A: Probability, 2nd year Mathematics (Michaelmas 2016)
- Part A: Statistics, 2nd year Mathematics (Hilary 2017)
Free University Berlin
- Statistics for Economists (2012, 2013, 2014), Introduction to probability and random variables, summary statistics, graphical data analysis
- Statistical Inference (2012) Point estimation, large sample properties and limiting theorems, confidence sets and tests, linear regression
- Statistical Modelling (2013) Generalised linear models, computational issues and implementation, application to economics.