Title: Handling Incomplete Heterogeneous Data using VAEs,
Abstract: Variational autoencoders (VAEs), as well as other generative models, have
been shown to be efficient and accurate to capture the latent structure of vast
amounts of complex high-dimensional data. However, existing VAEs can still not
directly handle data that are heterogenous (mixed continuous and discrete) or
incomplete (with missing data at random), which is indeed common in real-world
applications.
In this paper, we propose a general framework to design VAEs, suitable for
fitting incomplete heterogenous data. The proposed HI-VAE includes likelihood
models for real-valued, positive real valued, interval, categorical, ordinal
and count data, and allows to estimate (and potentially impute) missing data
accurately. Furthermore, HI-VAE presents competitive predictive performance in
supervised tasks, outperforming super- vised models when trained on incomplete
data