# Sebastian Schmon,

`DPhil (Oxon)`

Welcome to my webpage. I am an Assistant Professor in Statistics at Durham University and a Research Scientist in the Research team at Improbable. My research interests lie at the intersection of statistics, machine learning and probability theory as well as applications to the sciences. 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

#### AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise

with Julian Wyatt, Adam Leach and Chris G. Willcocks

*NTIRE, New Trends in Image Restoration and Enhancement workshop, CVPR 2022*

```
@InProceedings{Wyatt_2022_CVPR,
author = {Wyatt, Julian and Leach, Adam and Schmon, Sebastian M. and Willcocks, Chris G.},
title = {AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2022},
pages = {650-656}
}
```

#### Denoising diffusion probabilistic models on SO(3) for rotational alignment

with Adam Leach, Matteo T. Degiacomi and Chris G. Willcocks

*Workshop on Geometrical and Topological Representation Learning, ICLR 2022*

```
```

#### Black-box Bayesian inference for economic agent-based models

with Joel Dyer, Patrick Cannon and Doyne Farmer

*https://arxiv.org/abs/2202.00625*

```
@misc{dyer2022blackbox,
title={Black-box Bayesian inference for economic agent-based models},
author={Joel Dyer and Patrick Cannon and J. Doyne Farmer and Sebastian Schmon},
year={2022},
eprint={2202.00625},
archivePrefix={arXiv},
primaryClass={econ.EM}
}
```

#### 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}
}
```

#### Amortised Likelihood-free 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*

```
@InProceedings{pmlr-v151-dyer22a,
title = { Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation },
author = {Dyer, Joel and Cannon, Patrick W. and Schmon, Sebastian M.},
booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
pages = {11131--11144},
year = {2022},
editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
volume = {151},
series = {Proceedings of Machine Learning Research},
month = {28--30 Mar},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v151/dyer22a/dyer22a.pdf},
url = {https://proceedings.mlr.press/v151/dyer22a.html},
abstract = { Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference impossible. Recent advances in machine learning have introduced novel algorithms for estimating otherwise intractable likelihood functions using a likelihood ratio trick based on binary classifiers. Consequently, efficient likelihood approximations can be obtained whenever good probabilistic classifiers can be constructed. We propose a kernel classifier for sequential data using
```*path signatures* based on the recently introduced signature kernel. We demonstrate that the representative power of signatures yields a highly performant classifier, even in the crucially important case where sample numbers are low. In such scenarios, our approach can outperform sophisticated neural networks for common posterior inference tasks. }
}

#### 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 (Spotlight)*

```
@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, 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}
}
```

### Opportunities

#### Undergraduate and MSc Projects

I regularly offer projects relevant to my research areas. For the academic year 2022/23 you can find the project description below.

#### PhD Supervision

I am always looking for talented PhD students with an interested in Bayesian methods and/or Machine Learning. If you are interested in persuing a PhD under my supervision, please contact me at `sebastian [dot] schmon [at] durham [dot] ac [dot] uk`

.

As part of my position at Improbable I am currently co-supervising PhD students:

- Joel Dyer, Oxford
- Dan Ward, Bristol

### Teaching

### Durham University

#### Unsupervised Learning

Part of Advanced Statistical and Machine Learning: Foundations and Unsupervised Learning, MSc Scientific Computing and Data Analysis, 2022

### 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.