Learning to select for a predefined ranking. - Aleksandr Vorobev; Aleksei Ustimenko; Gleb Gusev; Pavel Serdyukov (ICML, 2019)

StochasticRank: Global Optimization of Scale-Free Discrete Functions. - Aleksei Ustimenko; Liudmila Prokhorenkova (ICML, 2020)

Uncertainty in Gradient Boosting via Ensembles. - Andrey Malinin; Liudmila Prokhorenkova; Aleksei Ustimenko (ICLR, 2021)

SGLB: Stochastic Gradient Langevin Boosting. - Aleksei Ustimenko; Liudmila Prokhorenkova (ICML, 2021)

Gradient Boosting Performs Low-Rank Gaussian Process Inference. - Aleksei Ustimenko; Artem Beliakov; Liudmila Prokhorenkova (CoRR, 2022)

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 (FIRE, 2023)

Gradient Boosting Performs Gaussian Process Inference. - Aleksei Ustimenko; Artem Beliakov; Liudmila Prokhorenkova (ICLR, 2023)

Which Tricks are Important for Learning to Rank? - Ivan Lyzhin; Aleksei Ustimenko; Andrey Gulin; Liudmila Prokhorenkova (ICML, 2023)

On (Normalised) Discounted Cumulative Gain as an Offline Evaluation Metric for Top-n Recommendation. - Olivier Jeunen; Ivan Potapov; Aleksei Ustimenko (CoRR, 2023)

Variance Reduction in Ratio Metrics for Efficient Online Experiments. - Shubham Baweja; Neeti Pokharna; Aleksei Ustimenko; Olivier Jeunen (ECIR (5), 2024)

Learning-to-Rank with Nested Feedback. - Hitesh Sagtani; Olivier Jeunen; Aleksei Ustimenko (ECIR (3), 2024)

Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting. - Aleksei Ustimenko; Aleksandr Beznosikov (ICLR, 2024)

Deep Stochastic Mechanics. - Elena Orlova; Aleksei Ustimenko; Ruoxi Jiang; Peter Y. Lu; Rebecca Willett (ICML, 2024)

On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation. - Olivier Jeunen; Ivan Potapov; Aleksei Ustimenko (KDD, 2024)

Learning Metrics that Maximise Power for Accelerated A/B-Tests. - Olivier Jeunen; Aleksei Ustimenko (KDD, 2024)

Powerful A/B-Testing Metrics and Where to Find Them. - Olivier Jeunen; Shubham Baweja; Neeti Pokharna; Aleksei Ustimenko (RecSys, 2024)

Multi-Objective Recommendation via Multivariate Policy Learning. - Olivier Jeunen; Jatin Mandav; Ivan Potapov; Nakul Agarwal; Sourabh Vaid; Wenzhe Shi; Aleksei Ustimenko (RecSys, 2024)

Δ-OPE: Off-Policy Estimation with Pairs of Policies. - Olivier Jeunen; Aleksei Ustimenko (RecSys, 2024)