  Point processes are stochastic models generating interacting points or events
in time, space, etc. Among characteristics of these models, first-order
intensity and conditional intensity functions are often considered. We focus on
inhomogeneous parametric forms of these functions assumed to depend on a
certain number of spatial covariates. When this number of covariates is large,
we are faced with a high-dimensional problem. This paper provides an overview
of these questions and existing solutions based on regularizations.
