Aleksei Ustimenko

According to our database, Aleksei Ustimenko authored at least 19 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Δ-OPE: Off-Policy Estimation with Pairs of Policies.

Olivier Jeunen, Aleksei Ustimenko

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

Multi-Objective Recommendation via Multivariate Policy Learning.

Olivier Jeunen, Jatin Mandav, Ivan Potapov, Nakul Agarwal, Sourabh Vaid, Wenzhe Shi, Aleksei Ustimenko

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

Powerful A/B-Testing Metrics and Where to Find Them.

Olivier Jeunen, Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

Learning Metrics that Maximise Power for Accelerated A/B-Tests.

Olivier Jeunen, Aleksei Ustimenko

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-<i>n</i> Recommendation.

Olivier Jeunen, Ivan Potapov, Aleksei Ustimenko

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Deep Stochastic Mechanics.

Elena Orlova, Aleksei Ustimenko, Ruoxi Jia, Peter Y. Lu, Rebecca Willett

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting.

Aleksei Ustimenko, Aleksandr Beznosikov

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Learning-to-Rank with Nested Feedback.

Hitesh Sagtani, Olivier Jeunen, Aleksei Ustimenko

Proceedings of the Advances in Information Retrieval, 2024

Variance Reduction in Ratio Metrics for Efficient Online Experiments.

Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko, Olivier Jeunen

Proceedings of the Advances in Information Retrieval, 2024

2023

On (Normalised) Discounted Cumulative Gain as an Offline Evaluation Metric for Top-n Recommendation.

Olivier Jeunen, Ivan Potapov, Aleksei Ustimenko

CoRR, 2023

Deep Stochastic Mechanics.

Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett

CoRR, 2023

Which Tricks are Important for Learning to Rank?

Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin, Liudmila Prokhorenkova

Proceedings of the International Conference on Machine Learning, 2023

Gradient Boosting Performs Gaussian Process Inference.

Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova

Proceedings of the Eleventh International Conference on Learning Representations, 2023

On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat.

Olivier Jeunen, Hitesh Sagtani, Himanshu Doi, Rasul Karimov, Neeti Pokharna, Md. Danish Kalim, Aleksei Ustimenko, Christopher Green, Rishabh Mehrotra, Wenzhe Shi

Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation, 2023

2022

Gradient Boosting Performs Low-Rank Gaussian Process Inference.

Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova

CoRR, 2022

2021

SGLB: Stochastic Gradient Langevin Boosting.

Aleksei Ustimenko, Liudmila Prokhorenkova

Proceedings of the 38th International Conference on Machine Learning, 2021

Uncertainty in Gradient Boosting via Ensembles.

Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

StochasticRank: Global Optimization of Scale-Free Discrete Functions.

Aleksei Ustimenko, Liudmila Prokhorenkova

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Learning to select for a predefined ranking.

Aleksandr Vorobev, Aleksei Ustimenko, Gleb Gusev, Pavel Serdyukov

Proceedings of the 36th International Conference on Machine Learning, 2019