  Temporal point processes are the dominant paradigm for modeling sequences of
events happening at irregular intervals. The standard way of learning in such
models is by estimating the conditional intensity function. However,
parameterizing the intensity function usually incurs several trade-offs. We
show how to overcome the limitations of intensity-based approaches by directly
modeling the conditional distribution of inter-event times. We draw on the
literature on normalizing flows to design models that are flexible and
efficient. We additionally propose a simple mixture model that matches the
flexibility of flow-based models, but also permits sampling and computing
moments in closed form. The proposed models achieve state-of-the-art
performance in standard prediction tasks and are suitable for novel
applications, such as learning sequence embeddings and imputing missing data.
