  In this work we propose a new algorithm for solving high-dimensional backward
stochastic differential equations (BSDEs). Based on the general
theta-discretization for the time-integrands, we show how to efficiently use
eXtreme Gradient Boosting (XGBoost) regression to approximate the resulting
conditional expectations in a quite high dimension. Numerical results
illustrate the efficiency and accuracy of our proposed algorithms for solving
very high-dimensional (up to $10000$ dimensions) nonlinear BSDEs.
