| Learning in POMDPs with Monte Carlo tree search S Katt, FA Oliehoek, C Amato International Conference on Machine Learning, 1819-1827, 2017 | 100 | 2017 |
| Online Planning for Target Object Search in Clutter under Partial Observability Y Xiao, S Katt, A ten Pas, S Chen, C Amato 2019 International Conference on Robotics and Automation (ICRA), 8241-8247, 2019 | 87 | 2019 |
| Bayesian Reinforcement Learning in Factored POMDPs S Katt, FA Oliehoek, C Amato Proceedings of the 18th International Conference on Autonomous Agents and …, 2019 | 60 | 2019 |
| Anytime versus real-time heuristic search for on-line planning B Cserna, M Bogochow, S Chambers, M Tremblay, S Katt, W Ruml Proceedings of the International Symposium on Combinatorial Search 7 (1 …, 2016 | 8 | 2016 |
| Removing dynamic objects for static scene reconstruction using light fields P Kaveti, S Katt, H Singh arXiv preprint arXiv:2003.11076, 2020 | 3 | 2020 |
| BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs S Katt, H Nguyen, FA Oliehoek, C Amato arXiv preprint arXiv:2202.08884, 2022 | 2 | 2022 |
| Efficient exploitation of factored domains in bayesian reinforcement learning for pomdps S Katt, FA Oliehoek, C Amato Adaptive Learning Agents, 2018 | 2 | 2018 |
| Conditional motion predictions E Wolff, S Katt, B Cserna US Patent 11,731,653, 2023 | 1 | 2023 |
| Introducing movements and animations to virtual victims in USARSim S Katt Bachelor's thesis, Universiteit van Amsterdam, 2012 | 1 | 2012 |
| Conditional motion predictions E Wolff, S Katt, B Cserna US Patent 11,814,075, 2023 | | 2023 |
| On-Robot Bayesian Reinforcement Learning for POMDPs H Nguyen, S Katt, Y Xiao, C Amato 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2023 | | 2023 |
| Bayesian Partially Observable Reinforcement Learning S Katt Northeastern University, 2023 | | 2023 |
| Bayesian RL in factored POMDPs S Katt, F Oliehoek, C Amato CEUR Workshop Proceedings 2491, 2019 | | 2019 |
| MCTS on model-based Bayesian Reinforcement Learning for efficient learning in Partially Observable environments S Katt, F Oliehoek, C Amato NeurIPS Workshop on Reinforcement Learning under Partial Observability, 2018 | | 2018 |
| Efficient Bayesian Learning in Factored Partially Observable Environments S Katt | | 2017 |
Christopher AmatoAssociate Professor at Northeastern UniversityVerificeret mail på ccs.neu.edu