  Traditional computational authorship attribution describes a classification
task in a closed-set scenario. Given a finite set of candidate authors and
corresponding labeled texts, the objective is to determine which of the authors
has written another set of anonymous or disputed texts. In this work, we
propose a probabilistic autoencoding framework to deal with this supervised
classification task. More precisely, we are extending a variational autoencoder
(VAE) with embedded Gaussian mixture model to a Student-$t$ mixture model.
Autoencoders have had tremendous success in learning latent representations.
However, existing VAEs are currently still bound by limitations imposed by the
assumed Gaussianity of the underlying probability distributions in the latent
space. In this work, we are extending the Gaussian model for the VAE to a
Student-$t$ model, which allows for an independent control of the "heaviness"
of the respective tails of the implied probability densities. Experiments over
an Amazon review dataset indicate superior performance of the proposed method.
