  Neural marked temporal point processes have been a valuable addition to the
existing toolbox of statistical parametric models for continuous-time event
data. These models are useful for sequences where each event is associated with
a single item (a single type of event or a "mark") -- but such models are not
suited for the practical situation where each event is associated with a set of
items. In this work, we develop a general framework for modeling set-valued
data in continuous-time, compatible with any intensity-based recurrent neural
point process model. In addition, we develop inference methods that can use
such models to answer probabilistic queries such as "the probability of item
$A$ being observed before item $B$," conditioned on sequence history. Computing
exact answers for such queries is generally intractable for neural models due
to both the continuous-time nature of the problem setting and the
combinatorially-large space of potential outcomes for each event. To address
this, we develop a class of importance sampling methods for querying with
set-based sequences and demonstrate orders-of-magnitude improvements in
efficiency over direct sampling via systematic experiments with four real-world
datasets. We also illustrate how to use this framework to perform model
selection using likelihoods that do not involve one-step-ahead prediction.
