34 citations · 45 across the 7 of their papers we have counts for
7 papers
On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top- Recommendation
Olivier Jeunen, Ivan Potapov, Aleksei Ustimenko
Approaches to recommendation are typically evaluated in one of two ways: (1) via a (simulated) online experiment, often seen as the gold standard, or (2) via some offline evaluatio…
Deep Stochastic Mechanics
Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang +2
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schrödinger equation inspired by stochastic mechanics and generative diffusio…
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…