  Point processes offer a versatile framework for sequential event modeling.
However, the computational challenges and constrained representational power of
the existing point process models have impeded their potential for wider
applications. This limitation becomes especially pronounced when dealing with
event data that is associated with multi-dimensional or high-dimensional marks
such as texts or images. To address this challenge, this study proposes a novel
event-generation framework for modeling point processes with high-dimensional
marks. We aim to capture the distribution of events without explicitly
specifying the conditional intensity or probability density function. Instead,
we use a conditional generator that takes the history of events as input and
generates the high-quality subsequent event that is likely to occur given the
prior observations. The proposed framework offers a host of benefits, including
considerable representational power to capture intricate dynamics in multi- or
even high-dimensional event space, as well as exceptional efficiency in
learning the model and generating samples. Our numerical results demonstrate
superior performance compared to other state-of-the-art baselines.
