  We develop new models and algorithms for learning the temporal dynamics of
the topic polytopes and related geometric objects that arise in topic model
based inference. Our model is nonparametric Bayesian and the corresponding
inference algorithm is able to discover new topics as the time progresses. By
exploiting the connection between the modeling of topic polytope evolution,
Beta-Bernoulli process and the Hungarian matching algorithm, our method is
shown to be several orders of magnitude faster than existing topic modeling
approaches, as demonstrated by experiments working with several million
documents in under two dozens of minutes.
