| Continual World: A Robotic Benchmark For Continual Reinforcement Learning M Wołczyk, M Zając, R Pascanu, Ł Kuciński, P Miłoś NeurIPS 2021, arXiv preprint arXiv:2105.10919, 2021 | 61 | 2021 |
| Complete discounted cash flow valuation L Gajek, Ł Kuciński Insurance: Mathematics and Economics 73, 1-19, 2017 | 29 | 2017 |
| Subgoal Search For Complex Reasoning Tasks K Czechowski, T Odrzygóźdź, M Zbysiński, M Zawalski, K Olejnik, Y Wu, ... NeurIPS 2021, arXiv preprint arXiv:2108.11204, 2021 | 25 | 2021 |
| Disentangling Transfer in Continual Reinforcement Learning M Wołczyk, M Zając, R Pascanu, Ł Kuciński, P Miłoś NeurIPS 2022, arXiv preprint arXiv:2209.13900, 2022 | 20 | 2022 |
| Magnushammer: A transformer-based approach to premise selection M Mikuła, S Antoniak, S Tworkowski, AQ Jiang, JP Zhou, C Szegedy, ... arXiv preprint arXiv:2303.04488, 2023 | 15 | 2023 |
| Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication Ł Kuciński, T Korbak, P Kołodziej, P Miłoś NeurIPS 2021, 2021 | 14 | 2021 |
| Continuous control with ensemble deep deterministic policy gradients P Januszewski, M Olko, M Królikowski, J Świątkowski, M Andrychowicz, ... NeurIPS Deep RL workshop 2021, arXiv preprint arXiv:2111.15382, 2021 | 8 | 2021 |
| Uncertainty-sensitive learning and planning with ensembles P Miłoś, Ł Kuciński, K Czechowski, P Kozakowski, M Klimek Uncertainty and Robustness in Deep Learning Workshop, ICML 2020, arXiv …, 2019 | 8 | 2019 |
| Interaction history as a source of compositionality in emergent communication T Korbak, J Zubek, Ł Kuciński, P Miłoś, J Rączaszek-Leonardi Interaction Studies 22 (2), 212-243, 2021 | 7 | 2021 |
| Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search M Zawalski, M Tyrolski, K Czechowski, D Stachura, P Piękos, ... ICLR 2023, arXiv preprint arXiv:2206.00702, 2022 | 6 | 2022 |
| Developmentally motivated emergence of compositional communication via template transfer T Korbak, J Zubek, Ł Kuciński, P Miłoś, J Rączaszek-Leonardi arXiv preprint arXiv:1910.06079, 2019 | 6 | 2019 |
| Emergence of compositional language in communication through noisy channel Ł Kuciński, P Kołodziej, P Miłoś Language in Reinforcement Learning Workshop at ICML 2020, 2020 | 4 | 2020 |
| Structure and Randomness in Planning and Reinforcement Learning K Czechowski, P Januszewski, P Kozakowski, Ł Kuciński, P Miłoś 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 2 | 2021 |
| To value a firm using DCF you must know its value: how to cope with this paradox L Gajek, Ł Kuciński Manuscript, 2012 | 2 | 2012 |
| Optimal risk sharing as a cooperative game Ł Kuciński Applicationes Mathematicae 2 (38), 219-242, 2011 | 2 | 2011 |
| The Role of Forgetting in Fine-Tuning Reinforcement Learning Models M Wolczyk, B Cupiał, M Ostaszewski, M Bortkiewicz, M Zając, R Pascanu, ... | 1 | 2023 |
| : Stochastic Time Series Modeling With Transformer Ł Kuciński, W Drzewakowski, M Olko, P Kozakowski, Ł Maziarka, ... arXiv preprint arXiv:2403.05713, 2024 | | 2024 |
| GUIDE: Guidance-based Incremental Learning with Diffusion Models B Cywiński, K Deja, T Trzciński, B Twardowski, Ł Kuciński arXiv preprint arXiv:2403.03938, 2024 | | 2024 |
| Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem M Wołczyk, B Cupiał, M Ostaszewski, M Bortkiewicz, M Zając, R Pascanu, ... arXiv preprint arXiv:2402.02868, 2024 | | 2024 |
| Structured Packing in LLM Training Improves Long Context Utilization K Staniszewski, S Tworkowski, S Jaszczur, H Michalewski, Ł Kuciński, ... arXiv preprint arXiv:2312.17296, 2023 | | 2023 |
Piotr MiłośUniversity of Warsaw, Polish Academy of Sciences and IDEAS NCBRVerified email at mimuw.edu.pl
