| Bayesian reinforcement learning in continuous POMDPs with Gaussian processes P Dallaire, C Besse, S Ross, B Chaib-draa 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2009 | 52 | 2009 |
| Learning Gaussian process models from uncertain data P Dallaire, C Besse, B Chaib-Draa Neural Information Processing: 16th International Conference, ICONIP 2009 …, 2009 | 39 | 2009 |
| Solving the continuous time multiagent patrol problem JS Marier, C Besse, B Chaib-Draa 2010 IEEE International Conference on Robotics and Automation, 941-946, 2010 | 35 | 2010 |
| An approximate inference with Gaussian process to latent functions from uncertain data P Dallaire, C Besse, B Chaib-Draa Neurocomputing 74 (11), 1945-1955, 2011 | 32 | 2011 |
| A Markov model for multiagent patrolling in continuous time JS Marier, C Besse, B Chaib-Draa Neural Information Processing: 16th International Conference, ICONIP 2009 …, 2009 | 16 | 2009 |
| Quasi-deterministic partially observable markov decision processes C Besse, B Chaib-Draa International Conference on Neural Information Processing, 237-246, 2009 | 12 | 2009 |
| Parallel Rollout for Online Solution of Dec-POMDPs. C Besse, B Chaib-draa FLAIRS Conference, 619-624, 2008 | 10 | 2008 |
| An efficient model for dynamic and constrained resource allocation problems C Besse, B Chaib-draa Proceedings of the 2nd International Workshop on Constraint Satisfaction …, 2007 | 10 | 2007 |
| Forecasting pharmacy purchases orders BK Almentero, J Li, C Besse 2021 IEEE 24th International Conference on Information Fusion (FUSION), 1-8, 2021 | 5 | 2021 |
| R-FRTDP: A real-time DP algorithm with tight bounds for a stochastic resource allocation problem C Besse, P Plamondon, B Chaib-Draa Advances in Artificial Intelligence: 20th Conference of the Canadian Society …, 2007 | 4 | 2007 |
| Why Natural Gradient for General Optimization? C Besse Departement Informatique, Universite Laval Sainte-Foy (Quebec), Canada, Tutorial, 2009 | 3 | 2009 |
| Recherche de conformation de molécules et apprentissage du potentiel de Lennard-Jones par systèmes multi-agents adaptatifs C BESSE-PATIN Institut de recherche informatique de Toulouse, 2005 | 2 | 2005 |
| Micro‐level reserving for general insurance claims using a long short‐term memory network I Chaoubi, C Besse, H Cossette, MP Côté Applied Stochastic Models in Business and Industry, 2023 | 1 | 2023 |
| Quasi Deterministic POMDPs and DecPOMDPs C Besse, B Chaib-draa Fifth Workshop on Multi-agent Sequential Decision Making in Uncertain …, 2010 | 1 | 2010 |
| Particle filter-based policy gradient in POMDPs C Besse Citeseer, 2009 | 1 | 2009 |
| Explainable Machine Learning for Drug Shortage Prediction in a Pandemic Setting J Li, BK Almentero, C Besse International Conference on Machine Learning, Optimization, and Data Science …, 2022 | | 2022 |
| Forecasting Conflicts Using N-Grams Models C Besse, A Bakhtiari, L Lamontagne Twenty-Fifth International FLAIRS Conference, 2012 | | 2012 |
| Forecasting Conflicts using N-gram Models A Bakhtiari, C Besse, L Lamontagne Second Workshop on Computational Social Science and the Wisdom of Crowds …, 2011 | | 2011 |
| Contraintes et observabilité dans les systèmes de Markov décentralisés C Besse Université Laval, 2010 | | 2010 |
| Stochastic Search using Natural Gradient C Besse | | 2009 |
Brahim Chaib-draaProfessor of Computer Science, Laval University, REPARTIVerified email at ift.ulaval.ca
