Search Results for author: Aleksei Ustimenko

Found 3 papers, 0 papers with code

Uncertainty in Gradient Boosting via Ensembles

no code implementations ICLR 2021 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.

General Classification

StochasticRank: Global Optimization of Scale-Free Discrete Functions

no code implementations ICML 2020 Aleksei Ustimenko, Liudmila Prokhorenkova

The problem is ill-posed due to the discrete structure of the loss, and to deal with that, we introduce two important techniques: stochastic smoothing and novel gradient estimate based on partial integration.

Global Optimization Learning-To-Rank

SGLB: Stochastic Gradient Langevin Boosting

no code implementations20 Jan 2020 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 provable generalization guarantees.

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