| Model-based reinforcement learning for atari L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ... arXiv preprint arXiv:1903.00374, 2019 | 1109 | 2019 |
| Simulation-based reinforcement learning for real-world autonomous driving B Osiński, A Jakubowski, P Zięcina, P Miłoś, C Galias, S Homoceanu, ... 2020 IEEE international conference on robotics and automation (ICRA), 6411-6418, 2020 | 180 | 2020 |
| Focused transformer: Contrastive training for context scaling S Tworkowski, K Staniszewski, M Pacek, Y Wu, H Michalewski, P Miłoś Advances in neural information processing systems 36, 42661-42688, 2023 | 118 | 2023 |
| Continual world: A robotic benchmark for continual reinforcement learning M Wołczyk, M Zając, R Pascanu, Ł Kuciński, P Miłoś Advances in Neural Information Processing Systems 34, 28496-28510, 2021 | 109 | 2021 |
| 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 | 108 | 2018 |
| Thor: Wielding hammers to integrate language models and automated theorem provers AQ Jiang, W Li, S Tworkowski, K Czechowski, T Odrzygóźdź, P Miłoś, ... Advances in Neural Information Processing Systems 35, 8360-8373, 2022 | 99 | 2022 |
| Inequality decomposition by population subgroups for ordinal data M Kobus, P Miłoś Journal of Health Economics 31 (1), 15-21, 2012 | 90 | 2012 |
| Maximal displacement of a supercritical branching random walk in a time-inhomogeneous random environment B Mallein, P Miłoś | 62* | |
| Moe-mamba: Efficient selective state space models with mixture of experts M Pióro, K Ciebiera, K Król, J Ludziejewski, M Krutul, J Krajewski, ... arXiv preprint arXiv:2401.04081, 2024 | 59 | 2024 |
| Magnushammer: A transformer-based approach to premise selection M Mikuła, S Tworkowski, S Antoniak, B Piotrowski, AQ Jiang, JP Zhou, ... arXiv preprint arXiv:2303.04488, 2023 | 38 | 2023 |
| Disentangling transfer in continual reinforcement learning M Wolczyk, M Zając, R Pascanu, Ł Kuciński, P Miłoś Advances in Neural Information Processing Systems 35, 6304-6317, 2022 | 37 | 2022 |
| CLT for Ornstein-Uhlenbeck branching particle system R Adamczak, P Miłoś | 35* | 2015 |
| CARLA Real Traffic Scenarios--novel training ground and benchmark for autonomous driving B Osiński, P Miłoś, A Jakubowski, P Zięcina, M Martyniak, C Galias, ... arXiv preprint arXiv:2012.11329, 2020 | 28 | 2020 |
| Subgoal search for complex reasoning tasks K Czechowski, T Odrzygóźdź, M Zbysiński, M Zawalski, K Olejnik, Y Wu, ... Advances in Neural Information Processing Systems 34, 624-638, 2021 | 26 | 2021 |
| The random interchange process on the hypercube R Kotecký, P Miłoś, D Ueltschi | 24 | 2016 |
| Catalytic role of noise and necessity of inductive biases in the emergence of compositional communication Ł Kuciński, T Korbak, P Kołodziej, P Miłoś Advances in neural information processing systems 34, 23075-23088, 2021 | 22 | 2021 |
| Delocalization of two-dimensional random surfaces with hard-core constraints P Miłoś, R Peled Communications in Mathematical Physics 340 (1), 1-46, 2015 | 22 | 2015 |
| Occupation time fluctuations of Poisson and equilibrium finite variance branching systems P Milos arXiv preprint math/0512414, 2005 | 22 | 2005 |
| On truncated variation, upward truncated variation and downward truncated variation for diffusions RM Łochowski, P Miłoś Stochastic Processes and their Applications 123 (2), 446-474, 2013 | 21 | 2013 |
| Overestimation, overfitting, and plasticity in actor-critic: the bitter lesson of reinforcement learning M Nauman, M Bortkiewicz, P Miłoś, T Trzciński, M Ostaszewski, M Cygan arXiv preprint arXiv:2403.00514, 2024 | 20 | 2024 |
Łukasz KucińskiPolish Academy of Sciences; University of Warsaw; IDEAS NCBRVerified email at impan.pl
