| Learning to run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments Ł Kidziński, SP Mohanty, CF Ong, Z Huang, S Zhou, A Pechenko, ... The NIPS'17 Competition: Building Intelligent Systems, 121-153, 2018 | 94 | 2018 |
| CORL: Research-oriented deep offline reinforcement learning library D Tarasov, A Nikulin, D Akimov, V Kurenkov, S Kolesnikov Advances in Neural Information Processing Systems 36, 2024 | 50 | 2024 |
| Artificial intelligence for prosthetics: Challenge solutions Ł Kidziński, C Ong, SP Mohanty, J Hicks, S Carroll, B Zhou, H Zeng, ... The NeurIPS'18 Competition: From Machine Learning to Intelligent …, 2020 | 46 | 2020 |
| Showing your offline reinforcement learning work: Online evaluation budget matters V Kurenkov, S Kolesnikov International Conference on Machine Learning, 11729-11752, 2022 | 23 | 2022 |
| Anti-exploration by random network distillation A Nikulin, V Kurenkov, D Tarasov, S Kolesnikov International Conference on Machine Learning, 26228-26244, 2023 | 17 | 2023 |
| Q-ensemble for offline rl: Don't scale the ensemble, scale the batch size A Nikulin, V Kurenkov, D Tarasov, D Akimov, S Kolesnikov arXiv preprint arXiv:2211.11092, 2022 | 13 | 2022 |
| LRWR: large-scale benchmark for lip reading in Russian language E Egorov, V Kostyumov, M Konyk, S Kolesnikov arXiv preprint arXiv:2109.06692, 2021 | 12 | 2021 |
| Run, skeleton, run: skeletal model in a physics-based simulation M Pavlov, S Kolesnikov, SM Plis arXiv preprint arXiv:1711.06922, 2017 | 12 | 2017 |
| Let offline rl flow: Training conservative agents in the latent space of normalizing flows D Akimov, V Kurenkov, A Nikulin, D Tarasov, S Kolesnikov arXiv preprint arXiv:2211.11096, 2022 | 10 | 2022 |
| Probabilistic embeddings revisited I Karpukhin, S Dereka, S Kolesnikov The Visual Computer, 1-14, 2023 | 9 | 2023 |
| TTRS: Tinkoff transactions recommender system benchmark S Kolesnikov, O Lashinin, M Pechatov, A Kosov arXiv preprint arXiv:2110.05589, 2021 | 7 | 2021 |
| Catalyst. rl: a distributed framework for reproducible rl research S Kolesnikov, O Hrinchuk arXiv preprint arXiv:1903.00027, 2019 | 7 | 2019 |
| Time-dependent next-basket recommendations S Naumov, M Ananyeva, O Lashinin, S Kolesnikov, DI Ignatov European Conference on Information Retrieval, 502-511, 2023 | 5 | 2023 |
| Sample efficient ensemble learning with catalyst. rl S Kolesnikov, V Khrulkov arXiv preprint arXiv:2003.14210, 2020 | 5 | 2020 |
| CVTT: Cross-validation through time M Andronov, S Kolesnikov arXiv preprint arXiv:2205.05393, 2022 | 3 | 2022 |
| Towards Interaction-based User Embeddings in Sequential Recommender Models. M Ananyeva, O Lashinin, V Ivanova, S Kolesnikov, DI Ignatov ORSUM@ RecSys, 2022 | 3 | 2022 |
| Make your next item recommendation model time sensitive E Makhneva, A Sverkunova, O Lashinin, M Ananyeva, S Kolesnikov Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation …, 2023 | 2 | 2023 |
| Prompts and Pre-Trained Language Models for Offline Reinforcement Learning D Tarasov, V Kurenkov, S Kolesnikov ICLR 2022 Workshop on Generalizable Policy Learning in Physical World, 2022 | 2 | 2022 |
| Deep Image Retrieval is not Robust to Label Noise S Dereka, I Karpukhin, S Kolesnikov Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 2 | 2022 |
| RecBaselines2023: a new dataset for choosing baselines for recommender models V Ivanova, O Lashinin, M Ananyeva, S Kolesnikov arXiv preprint arXiv:2306.14292, 2023 | 1 | 2023 |
Vladislav Kurenkovsomeone at someplace doing somewhat somehow在 innopolis.ru 的电子邮件经过验证
