seb

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

Rethinking Semi–Supervised Learning in VAEs

with Tom Joy, Philipp Torr, Siddharth Narayanaswamy and Tom Rainforth

arxiv.org/abs/2006.10102, submitted to Neurips 2020

We present an alternative approach to semi-supervision in variational autoencoders (VAEs) that incorporates labels through auxiliary variables rather than directly through the latent variables. Prior work has generally conflated the meaning of labels, i.e. the associated characteristics of interest, with the actual label values themselves—learning latent variables that directly correspond to the label values. We argue that to learn meaningful representations, semi-supervision should instead try to capture these richer characteristics and that the construction of latent variables as label values is not just unnecessary, but actively harmful. To this end, we develop a novel VAE model, the reparameterized VAE (ReVAE), which “reparameterises” supervision through auxiliary variables and a concomitant variational objective. Through judicious structuring of mappings between latent and auxiliary variables, we show that the ReVAE can effectively learn meaningful representations of data. In particular, we demonstrate that the ReVAE is able to match, and even improve on the classification accuracy of previous approaches, but more importantly, it also allows for more effective and more general interventions to be performed. 
@Article{Joy2020,
  author  = {Tom Joy, Philipp Torr, Siddharth Narayanaswamy and Tom Rainforth},
  title   = {Rethinking Semi–Supervised Learning in VAEs},
  journal = {https://arxiv.org/abs/2006.10102},
  year    = {2020},
}

Neural ODEs for Multi-state Survival Analysis

with Stefan Groha and Alexander Gusev

arxiv.org/abs/2006.04893, submitted to Neurips 2020

Survival models are a popular tool for the analysis of time to event data with applications in medicine, engineering, economics and many more. Advances like the Cox proportional hazard model have enabled researchers to better describe hazard rates for the occurrence of single fatal events, but are limited by modeling assumptions, like proportionality of hazard rates and linear effects. Moreover, common phenomena are often better described through multiple states, for example, the progress of a disease might be modeled as healthy, sick and dead instead of healthy and dead, where the competing nature of death and disease has to be taken into account. Also, individual characteristics can vary significantly between observational units, like patients, resulting in idiosyncratic hazard rates and different disease trajectories. These considerations require flexible modeling assumptions. Current standard models, however, are often ill-suited for such an analysis. To overcome these issues, we propose the use of neural ordinary differential equations as a flexible and general method for estimating multi-state survival models by directly solving the Kolmogorov forward equations. To quantify the uncertainty in the resulting individual cause-specific hazard rates, we further introduce a variational latent variable model. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting. 
@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)

The pseudo-marginal algorithm is a variant of the Metropolis–Hastings algorithm which samples asymptotically from a probability distribution when it is only possible to estimate unbiasedly an unnor- malized version of its density. Practically one has to trade-off the computational resources used to obtain this estimator against the asymptotic variances of the ergodic averages obtained by the pseudo-marginal algorithm. Recent works optimizing this trade-off rely on some strong assumptions which can cast doubts over their practical relevance. In particular, they all assume that the distribution of the additive error in the log-likelihood estimator is independent of the parameter value at which it is evaluated. Under weak regularity conditions we show here that, as the number of data points tends to infinity, a space-rescaled version of the pseudo-marginal chain converges weakly towards another pseudo-marginal chain for which this assumption indeed holds. A study of this limiting chain allows us to provide parameter dimension- dependent guidelines on how to optimally scale a normal random walk proposal and the number of Monte Carlo samples for the pseudo-marginal method in the large sample regime. This complements and vali- dates currently available results. 
@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

Bayesian approaches offer a robust statistical framework to introduce prior knowledge into learning procedures. However, the tasks introduced above can be challenging in practice since "information" gained by one network such as learned features can be difficult to encode into prior distributions over networks that do not share the same architecture or even the same output dimension. We introduce here a Bayesian viewpoint that centres around features and describe a set of prior distributions derived from the theory of Gaussian processes and deep kernel learning that facilitate a variety of deep learning tasks in a unified way. In particular, we will show that our approach is applicable to knowledge distillation, transfer learning and combining experts. 
@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

When the weights in a particle filter are not available analytically, standard resampling methods cannot be employed. To circumvent this problem state-of-the-art algorithms re- place the true weights with non-negative un- biased estimates. This algorithm is still valid but at the cost of higher variance of the re- sulting filtering estimates in comparison to a particle filter using the true weights. We pro- pose here a novel algorithm that allows for resampling according to the true intractable weights when only an unbiased estimator of the weights is available. We demonstrate our algorithm on several examples. 
@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

Modern systems of official statistics require the timely estimation of area-specific densities of subpopulations. Ideally estimates should be based on precise geocoded information, which is not available because of confidentiality constraints. One approach for ensuring confidentiality is by rounding the geoco‐ordinates. We propose multivariate non‐parametric kernel density estimation that reverses the rounding process by using a measurement error model. The methodology is applied to the Berlin register of residents for deriving density estimates of ethnic minorities and aged people. Estimates are used for identifying areas with a need for new advisory centres for migrants and infrastructure for older people. 
@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

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.