Sergey Kolesnikov
Sergey Kolesnikov
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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
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
CORL: Research-oriented deep offline reinforcement learning library
D Tarasov, A Nikulin, D Akimov, V Kurenkov, S Kolesnikov
arXiv preprint arXiv:2210.07105, 2022
Showing your offline reinforcement learning work: Online evaluation budget matters
V Kurenkov, S Kolesnikov
International Conference on Machine Learning, 11729-11752, 2022
Run, skeleton, run: skeletal model in a physics-based simulation
M Pavlov, S Kolesnikov, SM Plis
arXiv preprint arXiv:1711.06922, 2017
Probabilistic embeddings revisited
I Karpukhin, S Dereka, S Kolesnikov
The Visual Computer, 1-14, 2023
TTRS: Tinkoff transactions recommender system benchmark
S Kolesnikov, O Lashinin, M Pechatov, A Kosov
arXiv e-prints, arXiv: 2110.05589, 2021
LRWR: large-scale benchmark for lip reading in Russian language
E Egorov, V Kostyumov, M Konyk, S Kolesnikov
arXiv preprint arXiv:2109.06692, 2021
Sample efficient ensemble learning with catalyst. rl
S Kolesnikov, V Khrulkov
arXiv preprint arXiv:2003.14210, 2020
Catalyst. RL: a distributed framework for reproducible RL research
S Kolesnikov, O Hrinchuk
arXiv preprint arXiv:1903.00027, 2019
Anti-exploration by random network distillation
A Nikulin, V Kurenkov, D Tarasov, S Kolesnikov
arXiv preprint arXiv:2301.13616, 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
CVTT: Cross-validation through time
M Andronov, S Kolesnikov
arXiv preprint arXiv:2205.05393, 2022
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
Time-Dependent Next-Basket Recommendations
S Naumov, M Ananyeva, O Lashinin, S Kolesnikov, DI Ignatov
European Conference on Information Retrieval, 502-511, 2023
Towards interaction-based user embeddings in sequential recommender models
M Ananyeva, O Lashinin, V Ivanova, S Kolesnikov, DI Ignatov
Proceedings of the 5th Workshop on Online Recommender Systems and User …, 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
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
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
RecBaselines2023: a new dataset for choosing baselines for recommender models
V Ivanova, O Lashinin, M Ananyeva, S Kolesnikov
arXiv preprint arXiv:2306.14292, 2023
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