| Error propagation for approximate policy and value iteration A Farahmand, C Szepesvári, R Munos Advances in Neural Information Processing Systems (NeurIPS), 568-576, 2010 | 273 | 2010 |
| Regularized Policy Iteration A Farahmand, M Ghavamzadeh, S Mannor, C Szepesvári Advances in Neural Information Processing Systems 21 (NeurIPS 2008), 441-448, 2009 | 162 | 2009 |
| Manifold-adaptive dimension estimation A Farahmand, C Szepesvári, JY Audibert Proceedings of the 24th International Conference on Machine Learning (ICML …, 2007 | 139 | 2007 |
| Learning from Limited Demonstrations B Kim, A Farahmand, J Pineau, D Precup Advances in Neural Information Processing Systems (NeurIPS), 2859-2867, 2013 | 136 | 2013 |
| Value-aware loss function for model-based reinforcement learning A Farahmand, A Barreto, D Nikovski Artificial Intelligence and Statistics (AISTATS), 1486-1494, 2017 | 127 | 2017 |
| Regularized policy iteration with nonparametric function spaces A Farahmand, M Ghavamzadeh, C Szepesvári, S Mannor Journal of Machine Learning Research (JMLR) 17 (1), 4809-4874, 2016 | 124* | 2016 |
| Regularized fitted Q-iteration for planning in continuous-space Markovian decision problems A Farahmand, M Ghavamzadeh, C Szepesvári, S Mannor American Control Conference (ACC), 725-730, 2009 | 96* | 2009 |
| Robust jacobian estimation for uncalibrated visual servoing A Shademan, A Farahmand, M Jägersand IEEE International Conference on Robotics and Automation (ICRA), 5564-5569, 2010 | 86 | 2010 |
| Model Selection in Reinforcement Learning AM Farahmand, C Szepesvári Machine learning 85 (3), 299-332, 2011 | 74 | 2011 |
| Iterative Value-Aware Model Learning A Farahmand Advances in Neural Information Processing Systems (NeurIPS), 9072-9083, 2018 | 67 | 2018 |
| Action-Gap Phenomenon in Reinforcement Learning AM Farahmand Neural Information Processing Systems (NeurIPS), 2011 | 63 | 2011 |
| Global visual-motor estimation for uncalibrated visual servoing A Farahmand, A Shademan, M Jagersand IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS …, 2007 | 53* | 2007 |
| Deep reinforcement learning for partial differential equation control A Farahmand, S Nabi, DN Nikovski American Control Conference (ACC), 3120-3127, 2017 | 49 | 2017 |
| Regularization in Reinforcement Learning AM Farahmand Department of Computing Science, University of Alberta, 2011 | 45 | 2011 |
| Attentional network for visual object detection K Hara, MY Liu, O Tuzel, A Farahmand arXiv preprint arXiv:1702.01478, 2017 | 39 | 2017 |
| Model-based and model-free reinforcement learning for visual servoing A Farahmand, A Shademan, M Jagersand, C Szepesvári IEEE International Conference on Robotics and Automation (ICRA), 2917-2924, 2009 | 39* | 2009 |
| Policy-aware model learning for policy gradient methods R Abachi, M Ghavamzadeh, A Farahmand arXiv:2003.00030, 2020 | 35 | 2020 |
| Approximate MaxEnt Inverse Optimal Control and its Application for Mental Simulation of Human Interactions DA Huang, AM Farahmand, KM Kitani, JA Bagnell AAAI Conference on Artificial Intelligence (AAAI), 2015 | 32 | 2015 |
| Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learning M Akrout, A Farahmand, T Jarmain, L Abid International Conference on Medical Image Computing and Computer Assisted …, 2019 | 28 | 2019 |
| Method for Data-Driven Learning-based Control of HVAC Systems using High-Dimensional Sensory Observations A Farahmand, S Nabi, P Grover, DN Nikovski US Patent App. 15/290,038, 2018 | 28 | 2018 |
Csaba SzepesvariDeepMind & University of Alberta在 cs.ualberta.ca 的电子邮件经过验证
