Publications

Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, Dmitry Akimov, Sergey Kolesnikov (2022). Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size.

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Dmitry Akimov, Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov, Sergey Kolesnikov (2022). Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows.

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Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov, Sergey Kolesnikov (2022). CORL: Research-oriented Deep Offline Reinforcement Learning Library.

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Marina Ananyeva, Oleg Lashinin, Veronika Ivanova, Sergey Kolesnikov, Dmitry I Ignatov (2022). Towards Interaction-based User Embeddings in Sequential Recommender Models.

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Ivan A Karpukhin, Stanislav Dereka, Sergey Kolesnikov (2022). EXACT: How to Train Your Accuracy.

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Vladislav Kurenkov, Sergey Kolesnikov (2022). Showing your offline reinforcement learning work: Online evaluation budget matters.

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Stanislav Dereka, Ivan A Karpukhin, Sergey Kolesnikov (2022). Deep Image Retrieval Is Not Robust To Label Noise.

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Denis Tarasov, Vladislav Kurenkov, Sergey Kolesnikov (2022). Prompts and Pre-Trained Language Models for Offline Reinforcement Learning. Generalizable Policy Learning in the Physical World Workshop (ICLR 2022).

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Ivan Karpukhin, Stanislav Dereka, Sergey Kolesnikov (2022). Probabilistic Embeddings Revisited.

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Vladislav Kurenkov, Sergey Kolesnikov (2021). Showing your offline reinforcement learning work: Online evaluation budget matters. Offline Reinforcement Learning Workshop at Neural Information Processing Systems, 2021.

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Sergey Kolesnikov, Oleg Lashinin, Michail Pechatov, Alexander Kosov (2021). TTRS: Tinkoff Transactions Recommender System benchmark.

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Evgeniy Egorov, Vasily Kostyumov, Mikhail Konyk, Sergey Kolesnikov (2021). LRWR: Large-Scale Benchmark for Lip Reading in Russian language.

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Sergey Kolesnikov, Valentin Khrulkov (2020). Sample Efficient Ensemble Learning with Catalyst.RL.

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Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty, Jennifer Hicks, Sean Carroll, Bo Zhou, Hongsheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech Jaśkowski, Garrett Andersen, Odd Rune Lykkebø, Nihat Engin Toklu, Pranav Shyam, Rupesh Kumar Srivastava, Sergey Kolesnikov, Oleksii Hrinchuk, Anton Pechenko, Mattias Ljungström, Zhen Wang, Xu Hu, Zehong Hu, Minghui Qiu, Jun Huang, Aleksei Shpilman, Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Lance Rane, Aditya Bhatt, Zhengfei Wang, Penghui Qi, Zeyang Yu, Peng Peng, Quan Yuan, Wenxin Li, Yunsheng Tian, Ruihan Yang, Pingchuan Ma, Shauharda Khadka, Somdeb Majumdar, Zach Dwiel, Yinyin Liu, Evren Tumer, Jeremy Watson, Marcel Salathé, Sergey Levine, Scott Delp (2019). Artificial Intelligence for Prosthetics: Challenge Solutions. The NeurIPS ‘18 Competition.

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Sergey Kolesnikov, Oleksii Hrinchuk (2019). Catalyst.RL: A Distributed Framework for Reproducible RL Research.

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Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael F. Ong, Zhewei Huang, Shuchang Zhou, Anton Pechenko, Adam Stelmaszczyk, Piotr Jarosik, Mikhail Pavlov, Sergey Kolesnikov, Sergey Plis, Zhibo Chen, Zhizheng Zhang, Jiale ChenJun Shi, Zhuobin Zheng, Chun Yuan, Zhihui Lin, Henryk Michalewski, Piotr Milos, Blazej Osinski, Andrew Melnik, Malte Schilling, Helge Ritter, Sean F. Carroll, Jennifer Hicks, Sergey Levine, Marcel Salathé, Scott Delp (2018). Learning to Run Challenge Solutions: Adapting Reinforcement Learning Methods for Neuromusculoskeletal Environments. The NIPS ‘17 Competition: Building Intelligent Systems.

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Mikhail Pavlov, Sergey Kolesnikov, Sergey Plis (2018). Run, skeleton, run: skeletal model in a physics-based simulation. In AAAI.

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