# Theoretical Justifications for Random Forest

Is there any theoretical justifications for Random Forests in high dimensions? I notice the work "Uniform Convergence of Random Forests via Adaptive Concentration" which shows generalization of RF under low-dimensional settings where $d\ll n$. I was wondering if RF, or more generally, any tree-type algorithms works for high dimensional data, e.g., gene data, where we have $n = 100$ data points with $d = 10000$ dimensions.