  Despite its wide use and empirical successes, the theoretical understanding
and study of the behaviour and performance of the variational autoencoder (VAE)
have only emerged in the past few years. We contribute to this recent line of
work by analysing the VAE's reconstruction ability for unseen test data,
leveraging arguments from the PAC-Bayes theory. We provide generalisation
bounds on the theoretical reconstruction error, and provide insights on the
regularisation effect of VAE objectives. We illustrate our theoretical results
with supporting experiments on classical benchmark datasets.
