Abstract: ​Point processes are random sets which generalise the classical notion of a random (iid) sample by allowing i) the sample size to be random and/or ii) the sample points to be dependent. Therefore, point process have become ubiquitous in the modeling of spatial and/or temporal event data, e.g. earthquakes and disease cases. In this talk, we present the first statistical learning framework for general point processes, which is based on a subtle combination of two new concepts: prediction errors and cross-validation for point processes. The general idea is to split a point process in two, through thinning, and estimate parameters by predicting one part using the other. By repeating this procedure, we implicitly induce a conditional repeated sampling scheme. The proposed approach allows us to introduce a variety of loss functions not only suitable for standard spatial statistical problems but for general estimation settings, without imposing the iid assumptions. Having discussed different properties of this new approach, we illustrate how it may be applied in different spatial statistical settings and, numerically, we show in (at least) one of these settings that it outperforms the state of the art.