| Modification of UCT with patterns in Monte-Carlo Go S Gelly, Y Wang, O Teytaud, MU Patterns, P Tao | 467 | 2006 |
| The grand challenge of computer Go: Monte Carlo tree search and extensions S Gelly, L Kocsis, M Schoenauer, M Sebag, D Silver, C Szepesvári, ... Communications of the ACM 55 (3), 106-113, 2012 | 216 | 2012 |
| The computational intelligence of MoGo revealed in Taiwan's computer Go tournaments CS Lee, MH Wang, G Chaslot, JB Hoock, A Rimmel, O Teytaud, SR Tsai, ... IEEE Transactions on Computational Intelligence and AI in games 1 (1), 73-89, 2009 | 180 | 2009 |
| Continuous upper confidence trees A Couëtoux, JB Hoock, N Sokolovska, O Teytaud, N Bonnard International Conference on Learning and Intelligent Optimization, 433-445, 2011 | 107 | 2011 |
| Evaluation et validation de l’intérêt des règles d’association S Lallich, O Teytaud Revue des Nouvelles Technologies de l’Information 1 (2), 193-218, 2004 | 91 | 2004 |
| Association rule interestingness: Measure and statistical validation S Lallich, O Teytaud, E Prudhomme Quality measures in data mining, 251-275, 2007 | 90 | 2007 |
| Continuous lunches are free plus the design of optimal optimization algorithms A Auger, O Teytaud Algorithmica 57 (1), 121-146, 2010 | 83 | 2010 |
| Current frontiers in computer Go A Rimmel, O Teytaud, CS Lee, SJ Yen, MH Wang, SR Tsai IEEE Transactions on Computational Intelligence and AI in Games 2 (4), 229-238, 2010 | 81 | 2010 |
| Adding expert knowledge and exploration in Monte-Carlo Tree Search G Chaslot, C Fiter, JB Hoock, A Rimmel, O Teytaud Advances in Computer Games, 1-13, 2009 | 72 | 2009 |
| Optimally solving Dec-POMDPs as continuous-state MDPs JS Dibangoye, C Amato, O Buffet, F Charpillet Journal of Artificial Intelligence Research 55, 443-497, 2016 | 69 | 2016 |
| Creating an upper-confidence-tree program for Havannah F Teytaud, O Teytaud Advances in Computer Games, 65-74, 2009 | 66 | 2009 |
| Multi-armed bandit, dynamic environments and meta-bandits C Hartland, S Gelly, N Baskiotis, O Teytaud, M Sebag | 66 | 2006 |
| On the parallelization of Monte-Carlo planning S Gelly, JB Hoock, A Rimmel, O Teytaud, Y Kalemkarian | 63 | 2008 |
| Adaptive estimation of normals and surface area for discrete 3-D objects: application to snow binary data from X-ray tomography F Flin, JB Brzoska, D Coeurjolly, RA Pieritz, B Lesaffre, C Coleou, ... IEEE Transactions on Image Processing 14 (5), 585-596, 2005 | 63 | 2005 |
| General lower bounds for evolutionary algorithms O Teytaud, S Gelly Parallel Problem Solving from Nature-PPSN IX, 21-31, 2006 | 60 | 2006 |
| Optimal estimation for large-eddy simulation of turbulence and application to the analysis of subgrid models A Moreau, O Teytaud, JP Bertoglio Physics of fluids 18 (10), 105101, 2006 | 55 | 2006 |
| Scalability and parallelization of monte-carlo tree search A Bourki, G Chaslot, M Coulm, V Danjean, H Doghmen, JB Hoock, ... International Conference on Computers and Games, 48-58, 2010 | 49 | 2010 |
| Continuous lunches are free! A Auger, O Teytaud Proceedings of the 9th annual conference on Genetic and evolutionary …, 2007 | 49 | 2007 |
| Exploration vs exploitation vs safety: Risk-aware multi-armed bandits N Galichet, M Sebag, O Teytaud Asian Conference on Machine Learning, 245-260, 2013 | 48 | 2013 |
| Biasing Monte-Carlo simulations through RAVE values A Rimmel, F Teytaud, O Teytaud International Conference on Computers and Games, 59-68, 2010 | 47 | 2010 |
