| The development of an adaptive upper-limb stroke rehabilitation robotic system P Kan, R Huq, J Hoey, R Goetschalckx, A Mihailidis Journal of neuroengineering and rehabilitation 8, 1-18, 2011 | 115 | 2011 |
| Bayesian real-time dynamic programming S Sanner, R Goetschalckx, K Driessens, G Shani Proceedings of the 21st International Joint Conference on Artificial …, 2009 | 68 | 2009 |
| Imitation learning with demonstrations and shaping rewards K Judah, A Fern, P Tadepalli, R Goetschalckx Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 59 | 2014 |
| Continuous correlated beta processes R Goetschalckx, P Poupart, J Hoey IJCAI Proceedings-International Joint Conference on Artificial Intelligence …, 2011 | 18 | 2011 |
| A decision-theoretic approach in the design of an adaptive upper-limb stroke rehabilitation robot R Huq, P Kan, R Goetschalckx, D Hébert, J Hoey, A Mihailidis 2011 IEEE International Conference on Rehabilitation Robotics, 1-8, 2011 | 13 | 2011 |
| Multi-agent relational reinforcement learning K Tuyls, T Croonenborghs, J Ramon, R Goetschalckx, M Bruynooghe Proceedings of the First International Workshop on Learning and Adaptation …, 2005 | 13 | 2005 |
| Coactive learning for locally optimal problem solving R Goetschalckx, A Fern, P Tadepalli Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 12 | 2014 |
| Active Imitation Learning of Hierarchical Policies. M Hamidi, P Tadepalli, R Goetschalckx, A Fern IJCAI, 3554-3560, 2015 | 10 | 2015 |
| Cost-sensitive parsimonious linear regression R Goetschalckx, K Driessens, S Sanner 2008 Eighth IEEE International Conference on Data Mining, 809-814, 2008 | 10 | 2008 |
| Games with dynamic difficulty adjustment using pomdps R Goetschalckx, O Missura, J Hoey, T Gärtner ICML 2010 Workshop, 2010 | 8 | 2010 |
| Cost-sensitive reinforcement learning R Goetschalckx, K Driessens Proceedings of the workshop on AI Planning and Learning, 1-5, 2007 | 7 | 2007 |
| Multitask Coactive Learning. R Goetschalckx, A Fern, P Tadepalli IJCAI 14, 3518-3524, 2015 | 5 | 2015 |
| Reinforcement learning with the use of costly features R Goetschalckx, S Sanner, K Driessens European Workshop on Reinforcement Learning, 124-135, 2008 | 4 | 2008 |
| Adaptive submodularity with varying query sets: An application to active multi-label learning A Fern, R Goetschalckx, M Hamidi-Haines, P Tadepalli International Conference on Algorithmic Learning Theory, 577-592, 2017 | 3 | 2017 |
| An intuitive derivation of a sample size calculation J Hoey, R Goetschalckx School of Computer Science, University of Waterloo, 2010 | 3 | 2010 |
| Using expert knowledge to construct state-action aggregations for reinforcement learning R Goetschalckx, J Ramon, H Blockeel, M Bruynooghe CW Reports, 14-14, 2006 | 3 | 2006 |
| On policy learning in restricted policy spaces R Goetschalckx, J Ramon AAAI, 1858-1859, 2007 | 2 | 2007 |
| Active Learning of Hierarchical Policies from State-Action Trajectories M Hamidi, P Tadepalli, R Goetschalckx, A Fern Workshops at the 29th National Conference on Artificial Intelligence, 2015 | 1 | 2015 |
| Parsimonious Linear Model Trees R Goetschalckx, K Driessens Proc. of ICML Workshop on Machine Learning and Games, 2010 | 1 | 2010 |
| Using expert knowledge to construct error bound state-action aggregations for reinforcement learning R Goetschalckx, J Ramon Proceedings of the 19th Belgian-Dutch Conference on Artificial Intelligence …, 2007 | 1 | 2007 |
Jesse HoeyProfessor of Computer Science, University of WaterlooEmail confirmado em cs.uwaterloo.ca