Reviews: LightGBM: A Highly Efficient Gradient Boosting Decision Tree

The paper presents two nice ways for improving the usual gradient boosting algorithm where weak classifiers are decision trees. It is a paper oriented towards efficient (less costful) implementation of the usual algorithm in order to speed up the learning of decision trees by taking into account previous computations and sparse data. The approaches are interesting and smart. A risk bound is given for one of the improvements (GOSS), which seems sound but still quite loose: according to the experiments, a tighter bound could be obtained, getting rid of the "max" sizes of considered sets. No garantee is given for the second improvement (EFB) although is seems to be quite efficient in practice.