| Robot programming from demonstration, feedback and transfer Y Mollard, T Munzer, A Baisero, M Toussaint, M Lopes 2015 IEEE/RSJ international conference on intelligent robots and systems …, 2015 | 54 | 2015 |
| Unbiased asymmetric reinforcement learning under partial observability A Baisero, C Amato arXiv preprint arXiv:2105.11674, 2021 | 31* | 2021 |
| Leveraging fully observable policies for learning under partial observability H Nguyen, A Baisero, D Wang, C Amato, R Platt arXiv preprint arXiv:2211.01991, 2022 | 21 | 2022 |
| A deeper understanding of state-based critics in multi-agent reinforcement learning X Lyu, A Baisero, Y Xiao, C Amato Proceedings of the AAAI conference on artificial intelligence 36 (9), 9396-9404, 2022 | 19 | 2022 |
| Temporal segmentation of pair-wise interaction phases in sequential manipulation demonstrations A Baisero, Y Mollard, M Lopes, M Toussaint, I Lütkebohle 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015 | 19 | 2015 |
| On centralized critics in multi-agent reinforcement learning X Lyu, A Baisero, Y Xiao, B Daley, C Amato Journal of Artificial Intelligence Research 77, 295-354, 2023 | 11 | 2023 |
| Asymmetric DQN for partially observable reinforcement learning A Baisero, B Daley, C Amato Uncertainty in Artificial Intelligence, 107-117, 2022 | 11 | 2022 |
| Active goal recognition C Amato, A Baisero arXiv preprint arXiv:1909.11173, 2019 | 9 | 2019 |
| Equivariant reinforcement learning under partial observability HH Nguyen, A Baisero, D Klee, D Wang, R Platt, C Amato Conference on Robot Learning, 3309-3320, 2023 | 8 | 2023 |
| Semi-autonomous 3rd-hand robot M Lopes, J Peters, J Piater, M Toussaint, A Baisero, B Busch, O Erkent, ... Robot. Future Manuf. Scenar 3, 2015 | 8 | 2015 |
| Learning internal state models in partially observable environments A Baisero, C Amato Reinforcement Learning under Partial Observability, NeurIPS Workshop, 2018 | 7 | 2018 |
| The Path Kernel. A Baisero, FT Pokorny, D Kragic, CH Ek ICPRAM, 50-57, 2013 | 5 | 2013 |
| Hierarchical reinforcement learning under mixed observability H Nguyen, Z Yang, A Baisero, X Ma, R Platt, C Amato International Workshop on the Algorithmic Foundations of Robotics, 188-204, 2022 | 4 | 2022 |
| Learning Complementary Representations of the Past using Auxiliary Tasks in Partially Observable Reinforcement Learning. A Baisero, C Amato AAMAS, 1762-1764, 2020 | 4 | 2020 |
| On a family of decomposable kernels on sequences A Baisero, FT Pokorny, CH Ek arXiv preprint arXiv:1501.06284, 2015 | 4 | 2015 |
| The path kernel: A novel kernel for sequential data A Baisero, FT Pokorny, D Kragic, CH Ek Pattern Recognition Applications and Methods: International Conference …, 2014 | 2 | 2014 |
| On Stateful Value Factorization in Multi-Agent Reinforcement Learning E Marchesini, A Baisero, R Bathi, C Amato arXiv preprint arXiv:2408.15381, 2024 | 1 | 2024 |
| Reconciling Rewards with Predictive State Representations A Baisero, C Amato Proceedings of the Thirtieth International Joint Conference on Artificial …, 2021 | 1 | 2021 |
| Identification of Unmodeled Objects from Symbolic Descriptions A Baisero, S Otte, P Englert, M Toussaint arXiv preprint arXiv:1701.06450, 2017 | | 2017 |
| Encoding Sequential Structures using Kernels. A Baisero Royal Institute of Technology (KTH), 2012 | | 2012 |
Christopher AmatoAssociate Professor at Northeastern UniversityVerified email at ccs.neu.edu
