Seth Flaxman - Associate Professor at the Department of Computer Science, University of Oxford Data Science
Date: - Location: Eurecom
Abstract: Bayesian inference of models where the prior is a stochastic process, e.g. Gaussian process, 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 pVAE/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: pVAE (Mishra et al, 2022), PriorVAE (Semenova et al, 2022).
