  We present our submission to the Extreme Value Analysis 2021 Data Challenge
in which teams were asked to accurately predict distributions of wildfire
frequency and size within spatio-temporal regions of missing data. For the
purpose of this competition we developed a variant of the powerful variational
autoencoder models dubbed the Conditional Missing data Importance-Weighted
Autoencoder (CMIWAE). Our deep latent variable generative model requires little
to no feature engineering and does not necessarily rely on the specifics of
scoring in the Data Challenge. It is fully trained on incomplete data, with the
single objective to maximize log-likelihood of the observed wildfire
information. We mitigate the effects of the relatively low number of training
samples by stochastic sampling from a variational latent variable distribution,
as well as by ensembling a set of CMIWAE models trained and validated on
different splits of the provided data. The presented approach is not
domain-specific and is amenable to application in other missing data recovery
tasks with tabular or image-like information conditioned on auxiliary
information.
