34 citations · 44 across the 5 of their papers we have counts for
5 papers
Gradient Boosting Performs Gaussian Process Inference
Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova
This paper shows that gradient boosting based on symmetric decision trees can be equivalently reformulated as a kernel method that converges to the solution of a certain Kernel Rid…
Which Tricks Are Important for Learning to Rank?
Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin +1
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…
Uncertainty in Gradient Boosting via Ensembles
Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko
For many practical, high-risk applications, it is essential to quantify uncertainty in a model's predictions to avoid costly mistakes. While predictive uncertainty is widely studie…
StochasticRank: Global Optimization of Scale-Free Discrete Functions
Aleksei Ustimenko, Liudmila Prokhorenkova
In this paper, we introduce a powerful and efficient framework for direct optimization of ranking metrics. The problem is ill-posed due to the discrete structure of the loss, and t…
SGLB: Stochastic Gradient Langevin Boosting
Aleksei Ustimenko, Liudmila Prokhorenkova
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 p…