# Sebastian Schmon,

`DPhil (Oxon)`

Welcome to my webpage. I am an Assistant Professor in Statistics at Durham University and 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

#### Amortized inference for expensive time-series simulators with signatured ratio estimation

with Joel Dyer and Patrick Cannon

*The 25th International Conference on Artificial Intelligence and Statistics, AISTATS 2022*

```
@misc{dyer2022,
title={Amortized inference for expensive time-series simulators with signatured ratio estimation},
author={Joel Dyer and Patrick Cannon and Sebastian M Schmon},
year={2022},
journal={International Conference on Artificial Intelligence and Statistics}
}
```

#### Approximate Bayesian Computation with Path Signatures

with Joel Dyer and Patrick Cannon

*https://arxiv.org/abs/2106.12555*

```
@misc{dyer2021approximate,
title={Approximate Bayesian Computation with Path Signatures},
author={Joel Dyer and Patrick Cannon and Sebastian M Schmon},
year={2021},
eprint={2106.12555},
archivePrefix={arXiv},
primaryClass={stat.ME}
}
```

#### Learning Multimodal VAEs through Mutual Supervision

with Tom Joy, Yuge Shi, Philip H.S. Torr, Tom Rainforth, and N. Siddharth

*International Conference on Learning Representations, ICLR 2022*

```
@misc{joy2021learning,
title={Learning Multimodal VAEs through Mutual Supervision},
author={Tom Joy and Yuge Shi and Philip H. S. Torr and Tom Rainforth and Sebastian M. Schmon and N. Siddharth},
year={2021},
eprint={2106.12570},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```

#### Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics

with Philippe Gagnon

*Statistics and Computing, forthcoming 2022*

```
@Article{bvm_mcmc,
author = {Schmon, Sebastian M and Philippe Gagnon},
title = {Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics},
journal = {https://arxiv.org/abs/2104.06384},
year = {2021},
}
```

#### 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}
}
```

#### Large Sample Asymptotics of the Pseudo-Marginal Method

with Arnaud Doucet, George Deligiannidis and Mike Pitt

*Biometrika, 2021*

```
@Article{SchmonDeligiannidisDoucet2018a,
author = {Schmon, S M and Deligiannidis, G and Doucet, A and Pitt, M K},
title = "{Large-sample asymptotics of the pseudo-marginal method}",
journal = {Biometrika},
volume = {108},
number = {1},
pages = {37-51},
year = {2021},
month = {03},
issn = {0006-3444},
doi = {10.1093/biomet/asaa044},
url = {https://doi.org/10.1093/biomet/asaa044},
eprint = {https://academic.oup.com/biomet/article-pdf/108/1/37/36440998/asaa044.pdf},
}
```

#### Generalized Posteriors in Approximate Bayesian Computation

with Patrick W Cannon and Jeremias Knoblauch

*The 3rd Symposium on Advances in Approximate Bayesian Inference, AABI 2020*

```
@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},
}
```

#### 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.