\dm_csml_event_details UCL ELLIS

Deep generative modelling with πVAE and PriorVAE to enable scalable MCMC inference on stochastic processes


Seth Flaxman


University of Oxford


Friday, 28 April 2023




Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH



Event series

DeepMind/ELLIS CSML Seminar Series


Bayesian inference of models where the prior is a stochastic process, e.g. Gaussian process models, are ubiquitous in applied fields where both the flexibility of models and accurate uncertainty quantification are of importance. Decades of research have attempted to alleviate well-known computational bottlenecks, to varying degrees of success. We describe two new related approaches to encoding Gaussian process priors or their finite realisations using deep generative models (VAEs). In our πVAE/PriorVAE framework, trained decoders replace the original prior during Markov chain Monte Carlo (MCMC) inference, conveniently enabling any probabilistic programming framework to sample from complex, nonparametric priors. This approach enables fast and highly efficient inference, with orders-of-magnitude speedups in MCMC efficiency after paying a one-off cost to train a deep neural network. We will describe recent work to enable the recovery of interpretable hyperparameters for these models and applications to spatiotemporal disease modelling. Relevant papers: πVAE (Mishra et al, 2022; https://link.springer.com/article/10.1007/s11222-022-10151-w), PriorVAE (Semenova et al, 2022; https://royalsocietypublishing.org/doi/full/10.1098/rsif.2022.0094), PriorCVAE (Semenova et al, 2023; https://arxiv.org/abs/2304.04307).


Seth Flaxman is an Associate Professor in the Department of Computer Science at Oxford. His PhD is in machine learning and public policy from Carnegie Mellon University. He was part of the Imperial College COVID-19 Response Team and has published widely on computational statistics and statistical machine learning. He helps run the Machine Learning & Global Health Network (MLGH.net). He was awarded the Samsung AI Researcher of the Year Award in 2020, and the SPI-M-O Award for Modelling and Data Support (SAMDS), in recognition of epidemiological and modelling advice provided to UK government during the Covid-19 pandemic. His research is supported by an EPSRC fellowship.