  This paper introduces Stochastic Gradient Langevin Boosting (SGLB) - a
powerful and efficient machine learning framework that may deal with a wide
range of loss functions and has provable generalization guarantees. The method
is based on a special form of the Langevin diffusion equation specifically
designed for gradient boosting. This allows us to theoretically guarantee the
global convergence even for multimodal loss functions, while standard gradient
boosting algorithms can guarantee only local optimum. We also empirically show
that SGLB outperforms classic gradient boosting when applied to classification
tasks with 0-1 loss function, which is known to be multimodal.
