Aleksei Ustimenko

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 27

Top Topics

Gradient Boosting

Boosted Decision Trees

Relevance Judgments

Global Optimization

Top Venues

Publications

Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko, Olivier Jeunen
Variance Reduction in Ratio Metrics for Efficient Online Experiments. CoRR (2024)
Aleksei Ustimenko, Aleksandr Beznosikov
Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting. ICLR (2024)
Hitesh Sagtani, Olivier Jeunen, Aleksei Ustimenko
Learning-to-Rank with Nested Feedback. CoRR (2024)
Hitesh Sagtani, Olivier Jeunen, Aleksei Ustimenko
Learning-to-Rank with Nested Feedback. ECIR (3) (2024)
Olivier Jeunen, Aleksei Ustimenko
Learning Metrics that Maximise Power for Accelerated A/B-Tests. CoRR (2024)
Olivier Jeunen, Ivan Potapov, Aleksei Ustimenko
Recommendation. KDD (2024)
Olivier Jeunen, Aleksei Ustimenko
Learning Metrics that Maximise Power for Accelerated A/B-Tests. KDD (2024)
Olivier Jeunen, Jatin Mandav, Ivan Potapov, Nakul Agarwal, Sourabh Vaid, Wenzhe Shi, Aleksei Ustimenko
Multi-Objective Recommendation via Multivariate Policy Learning. CoRR (2024)
Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko, Olivier Jeunen
Variance Reduction in Ratio Metrics for Efficient Online Experiments. ECIR (5) (2024)
Olivier Jeunen, Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko
Powerful A/B-Testing Metrics and Where to Find Them. CoRR (2024)
Olivier Jeunen, Aleksei Ustimenko
Δ-OPE: Off-Policy Estimation with Pairs of Policies. CoRR (2024)
Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett
Deep Stochastic Mechanics. CoRR (2023)
Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova
Gradient Boosting Performs Gaussian Process Inference. ICLR (2023)
Olivier Jeunen, Ivan Potapov, Aleksei Ustimenko
On (Normalised) Discounted Cumulative Gain as an Offline Evaluation Metric for Top-n Recommendation. CoRR (2023)
Olivier Jeunen, Hitesh Sagtani, Himanshu Doi, Rasul Karimov, Neeti Pokharna, Danish Kalim, Aleksei Ustimenko, Christopher Green, Wenzhe Shi, Rishabh Mehrotra
On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat. CoRR (2023)
Olivier Jeunen, Hitesh Sagtani, Himanshu Doi, Rasul Karimov, Neeti Pokharna, Danish Kalim, Aleksei Ustimenko, Christopher Green, Rishabh Mehrotra, Wenzhe Shi
On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat. FIRE (2023)
Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin, Liudmila Prokhorenkova
Which Tricks are Important for Learning to Rank? ICML (2023)
Aleksei Ustimenko, Aleksandr Beznosikov
Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting. CoRR (2023)
Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin, Liudmila Prokhorenkova
Which Tricks are Important for Learning to Rank? CoRR (2022)
Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova
Gradient Boosting Performs Low-Rank Gaussian Process Inference. CoRR (2022)
Aleksei Ustimenko, Liudmila Prokhorenkova
SGLB: Stochastic Gradient Langevin Boosting. ICML (2021)
Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko
Uncertainty in Gradient Boosting via Ensembles. ICLR (2021)
Aleksei Ustimenko, Liudmila Prokhorenkova
StochasticRank: Global Optimization of Scale-Free Discrete Functions. ICML (2020)
Aleksei Ustimenko, Liudmila Prokhorenkova, Andrey Malinin
Uncertainty in Gradient Boosting via Ensembles. CoRR (2020)
Aleksei Ustimenko, Liudmila Prokhorenkova
StochasticRank: Global Optimization of Scale-Free Discrete Functions. CoRR (2020)
Aleksei Ustimenko, Liudmila Prokhorenkova
SGLB: Stochastic Gradient Langevin Boosting. CoRR (2020)
Aleksandr Vorobev, Aleksei Ustimenko, Gleb Gusev, Pavel Serdyukov
Learning to select for a predefined ranking. ICML (2019)