Sebastian Schmon,
DPhil (Oxon)
Welcome to my webpage. I am a machine learning researcher, statistician and AI enthusiast. Before moving to industry full-time, I worked as 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 as well as applications to the sciences. My recent work involves, for instance, work on generative AI including large language models, embeddings and diffusion models. 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.
Projects
TextInsight
A hobby project to experiment with language models as well as their use for combating misinformation and polarization.
Unpublished Preprints
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference
with Patrick Cannon and Daniel Ward
https://arxiv.org/abs/2209.01845
@article{cannon2022investigating,
title={Investigating the Impact of Model Misspecification in Neural Simulation-based Inference},
author={Cannon, Patrick and Ward, Daniel and Schmon, Sebastian M},
journal={arXiv preprint arXiv:2209.01845},
year={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}
}
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},
}
Publications (Peer Reviewed)
Robust Neural Posterior Estimation and Statistical Model Criticism
with Daniel Ward, Patrick Cannon, Mark Beaumont and Matteo Fasiolo
Neural Information Processig Systems, NeurIPS 2022
@article{ward2022,
title={Robust Neural Posterior Estimation and Statistical Model Criticism},
author={Ward, Daniel and Cannon, Patrick and Beaumont, Mark and Fasiolo, Matteo and Schmon, Sebastian M},
journal={Neural Information Processing Sytems},
year={2022}
}
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},
}
Capturing 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},
}
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}
}
Workshop Papers
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning
with Jordan Langham-Lopez and Patrick Cannon
AI4ABM Workshop, ICML 2022
@article{langham2022high,
title={High Performance Simulation for Scalable Multi-Agent Reinforcement Learning},
author={Langham-Lopez, Jordan and Schmon, Sebastian M and Cannon, Patrick},
journal={arXiv preprint arXiv:2207.03945},
year={2022}
}
Calibrating Agent-based Models to Microdata with Graph Neural Networks
with Joel Dyer and Patrick Cannon
AI4ABM Workshop, ICML 2022 (Best Paper Award)
@article{dyer2022calibrating,
title={Calibrating Agent-based Models to Microdata with Graph Neural Networks},
author={Dyer, Joel and Cannon, Patrick and Farmer, J Doyne and Schmon, Sebastian M},
journal={arXiv preprint arXiv:2206.07570},
year={2022}
}
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
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},
}
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.