| 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 | 39 | 2021 |
| Complete discounted cash flow valuation L Gajek, Ł Kuciński Insurance: Mathematics and Economics 73, 1-19, 2017 | 24 | 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 | 14 | 2021 |
| 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 | 11 | 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 |
| Disentangling Transfer in Continual Reinforcement Learning M Wołczyk, M Zając, R Pascanu, Ł Kuciński, P Miłoś NeurIPS 2022, arXiv preprint arXiv:2209.13900, 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 |
| 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 | 5 | 2021 |
| 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 | 4 | 2023 |
| 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 | 4 | 2021 |
| Emergence of compositional language in communication through noisy channel Ł Kuciński, P Kołodziej, P Miłoś Language in Reinforcement Learning Workshop at ICML 2020, 2020 | 3 | 2020 |
| Optimal risk sharing as a cooperative game Ł Kuciński Applicationes Mathematicae 2 (38), 219-242, 2011 | 3 | 2011 |
| 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 |
| 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 | 1 | 2022 |
| On The Role of Forgetting in Fine-Tuning Reinforcement Learning Models M Wolczyk, B Cupiał, M Zając, R Pascanu, Ł Kuciński, P Miłoś Workshop on Reincarnating Reinforcement Learning at ICLR 2023, 2023 | | 2023 |
| Trust Your : Gradient-based Intervention Targeting for Causal Discovery M Olko, M Zając, A Nowak, N Scherrer, Y Annadani, S Bauer, Ł Kuciński, ... Causal Machine Learning for Real-World Impact Workshop, NeurIPS 2022, arXiv …, 2022 | | 2022 |
| Trust, but Verify: Alleviating Pessimistic Errors in Model-Based Exploration K Czechowski, T Odrzygóźdź, M Izworski, M Zbysiński, Ł Kuciński, P Miłoś 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | | 2021 |
| Structure and randomness in planning and reinforcement learning P Kozakowski, P Januszewski, K Czechowski, Ł Kuciński, P Miłoś DRL Workshop, NeurIPS 2020, 2020 | | 2020 |
| Equivalence of measures corresponding to the Hilbert space valued Lévy processes Ł Kuciński IM PAN Preprint 686 (686), 2007 | | 2007 |
Piotr MiłośUniversity of Warsaw, Polish Academy of SciencesVerified email at mimuw.edu.pl
