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ORCIDAleksei Ustimenko
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2020 – today
- 2024
[c16]Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko, Olivier Jeunen:
Variance Reduction in Ratio Metrics for Efficient Online Experiments. ECIR (5) 2024: 292-297- [c15]Hitesh Sagtani
, Olivier Jeunen, Aleksei Ustimenko:
Learning-to-Rank with Nested Feedback. ECIR (3) 2024: 306-315 - [c14]Aleksei Ustimenko, Aleksandr Beznosikov:
Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting. ICLR 2024 - [c13]Elena Orlova, Aleksei Ustimenko, Ruoxi Jia, Peter Y. Lu, Rebecca Willett:
Deep Stochastic Mechanics. ICML 2024 - [c12]Olivier Jeunen, Ivan Potapov, Aleksei Ustimenko:
On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation. KDD 2024: 1222-1233 - [c11]Olivier Jeunen, Aleksei Ustimenko:
Learning Metrics that Maximise Power for Accelerated A/B-Tests. KDD 2024: 5183-5193 - [c10]Olivier Jeunen, Jatin Mandav, Ivan Potapov, Nakul Agarwal, Sourabh Vaid, Wenzhe Shi, Aleksei Ustimenko:
Multi-Objective Recommendation via Multivariate Policy Learning. RecSys 2024: 712-721 - [c9]Olivier Jeunen, Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko:
Powerful A/B-Testing Metrics and Where to Find Them. RecSys 2024: 816-818 - [c8]Olivier Jeunen, Aleksei Ustimenko:
Δ-OPE: Off-Policy Estimation with Pairs of Policies. RecSys 2024: 878-883 - [i15]Hitesh Sagtani, Olivier Jeunen, Aleksei Ustimenko:
Learning-to-Rank with Nested Feedback. CoRR abs/2401.04053 (2024) - [i14]Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko, Olivier Jeunen:
Variance Reduction in Ratio Metrics for Efficient Online Experiments. CoRR abs/2401.04062 (2024) - [i13]Olivier Jeunen, Aleksei Ustimenko:
Learning Metrics that Maximise Power for Accelerated A/B-Tests. CoRR abs/2402.03915 (2024) - [i12]Olivier Jeunen, Jatin Mandav, Ivan Potapov, Nakul Agarwal, Sourabh Vaid, Wenzhe Shi, Aleksei Ustimenko:
Multi-Objective Recommendation via Multivariate Policy Learning. CoRR abs/2405.02141 (2024) - [i11]Olivier Jeunen, Aleksei Ustimenko:
Δ-OPE: Off-Policy Estimation with Pairs of Policies. CoRR abs/2405.10024 (2024) - [i10]Olivier Jeunen, Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko:
Powerful A/B-Testing Metrics and Where to Find Them. CoRR abs/2407.20665 (2024) - 2023
- [c7]Olivier Jeunen, Hitesh Sagtani, Himanshu Doi, Rasul Karimov, Neeti Pokharna, Md. 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: 136-141 - [c6]Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova:
Gradient Boosting Performs Gaussian Process Inference. ICLR 2023 - [c5]Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin, Liudmila Prokhorenkova:
Which Tricks are Important for Learning to Rank? ICML 2023: 23264-23278 - [i9]Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett:
Deep Stochastic Mechanics. CoRR abs/2305.19685 (2023) - [i8]Olivier Jeunen, Ivan Potapov, Aleksei Ustimenko:
On (Normalised) Discounted Cumulative Gain as an Offline Evaluation Metric for Top-n Recommendation. CoRR abs/2307.15053 (2023) - [i7]Aleksei Ustimenko, Aleksandr Beznosikov:
Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting. CoRR abs/2310.06081 (2023) - [i6]Olivier Jeunen, Hitesh Sagtani, Himanshu Doi, Rasul Karimov, Neeti Pokharna, Md. 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 abs/2312.01760 (2023) - 2022
- [i5]Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin, Liudmila Prokhorenkova:
Which Tricks are Important for Learning to Rank? CoRR abs/2204.01500 (2022) - [i4]Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova:
Gradient Boosting Performs Low-Rank Gaussian Process Inference. CoRR abs/2206.05608 (2022) - 2021
- [c4]Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko:
Uncertainty in Gradient Boosting via Ensembles. ICLR 2021 - [c3]Aleksei Ustimenko, Liudmila Prokhorenkova:
SGLB: Stochastic Gradient Langevin Boosting. ICML 2021: 10487-10496 - 2020
- [c2]Aleksei Ustimenko, Liudmila Prokhorenkova:
StochasticRank: Global Optimization of Scale-Free Discrete Functions. ICML 2020: 9669-9679 - [i3]Aleksei Ustimenko, Liudmila Prokhorenkova:
SGLB: Stochastic Gradient Langevin Boosting. CoRR abs/2001.07248 (2020) - [i2]Aleksei Ustimenko, Liudmila Prokhorenkova:
StochasticRank: Global Optimization of Scale-Free Discrete Functions. CoRR abs/2003.02122 (2020) - [i1]Aleksei Ustimenko, Liudmila Prokhorenkova, Andrey Malinin:
Uncertainty in Gradient Boosting via Ensembles. CoRR abs/2006.10562 (2020)
2010 – 2019
- 2019
- [c1]Aleksandr Vorobev, Aleksei Ustimenko, Gleb Gusev, Pavel Serdyukov:
Learning to select for a predefined ranking. ICML 2019: 6477-6486
Coauthor Index
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last updated on 2024-10-23 21:28 CEST by the dblp team
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