  Nowadays, state-of-the-art learning-to-rank methods are based on
gradient-boosted decision trees (GBDT). The most well-known algorithm is
LambdaMART which was proposed more than a decade ago. Recently, several other
GBDT-based ranking algorithms were proposed. In this paper, we thoroughly
analyze these methods in a unified setup. In particular, we address the
following questions. Is direct optimization of a smoothed ranking loss
preferable over optimizing a convex surrogate? How to properly construct and
smooth surrogate ranking losses? To address these questions, we compare
LambdaMART with YetiRank and StochasticRank methods and their modifications. We
also propose a simple improvement of the YetiRank approach that allows for
optimizing specific ranking loss functions. As a result, we gain insights into
learning-to-rank techniques and obtain a new state-of-the-art algorithm.
