  Current implementations of Gradient Boosting Machines are mostly designed for
single-target regression tasks and commonly assume independence between
responses when used in multivariate settings. As such, these models are not
well suited if non-negligible dependencies exist between targets. To overcome
this limitation, we present an extension of XGBoostLSS that models multiple
targets and their dependencies in a probabilistic regression setting. Empirical
results show that our approach outperforms existing GBMs with respect to
runtime and compares well in terms of accuracy.
