| Collaborative hyperparameter tuning R Bardenet, M Brendel, B Kégl, M Sebag International conference on machine learning, 199-207, 2013 | 498 | 2013 |
| Extending population-based incremental learning to continuous search spaces M Sebag, A Ducoulombier International Conference on Parallel Problem Solving from Nature, 418-427, 1998 | 325 | 1998 |
| 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 | 324 | 2012 |
| Treefinder: a first step towards xml data mining A Termier, MC Rousset, M Sebag 2002 IEEE International Conference on Data Mining, 2002. Proceedings., 450-457, 2002 | 283 | 2002 |
| Causal generative neural networks O Goudet, D Kalainathan, P Caillou, I Guyon, D Lopez-Paz, M Sebag arXiv preprint arXiv:1711.08936, 2017 | 221* | 2017 |
| April: Active preference learning-based reinforcement learning R Akrour, M Schoenauer, M Sebag Joint European Conference on Machine Learning and Knowledge Discovery in …, 2012 | 199 | 2012 |
| Adaptive operator selection with dynamic multi-armed bandits L DaCosta, A Fialho, M Schoenauer, M Sebag Proceedings of the 10th annual conference on Genetic and evolutionary …, 2008 | 198 | 2008 |
| Preference-based policy learning R Akrour, M Schoenauer, M Sebag Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011 | 194 | 2011 |
| Analyzing bandit-based adaptive operator selection mechanisms A Fialho, L Da Costa, M Schoenauer, M Sebag Annals of Mathematics and Artificial Intelligence 60 (1), 25-64, 2010 | 177 | 2010 |
| Learning functional causal models with generative neural networks O Goudet, D Kalainathan, P Caillou, I Guyon, D Lopez-Paz, M Sebag Explainable and Interpretable Models in Computer Vision and Machine Learning …, 2018 | 171 | 2018 |
| Analysis of the AutoML Challenge Series 2015–2018 I Guyon, L Sun-Hosoya, M Boullé, HJ Escalante, S Escalera, Z Liu, ... Automated Machine Learning, 177-219, 2019 | 158 | 2019 |
| Analysis of the AutoML Challenge Series I Guyon, L Sun-Hosoya, M Boullé, HJ Escalante, S Escalera, Z Liu, ... Automated Machine Learning, 177, 2019 | 158 | 2019 |
| Exploration vs exploitation vs safety: Risk-aware multi-armed bandits N Galichet, M Sebag, O Teytaud Asian Conference on Machine Learning, 245-260, 2013 | 152 | 2013 |
| BenchNN: On the broad potential application scope of hardware neural network accelerators T Chen, Y Chen, M Duranton, Q Guo, A Hashmi, M Lipasti, A Nere, S Qiu, ... 2012 IEEE International Symposium on Workload Characterization (IISWC), 36-45, 2012 | 138 | 2012 |
| Extreme value based adaptive operator selection Á Fialho, L Da Costa, M Schoenauer, M Sebag International Conference on Parallel Problem Solving from Nature, 175-184, 2008 | 137 | 2008 |
| Multi-armed bandit, dynamic environments and meta-bandits C Hartland, S Gelly, N Baskiotis, O Teytaud, M Sebag | 129 | 2006 |
| Comparison-based optimizers need comparison-based surrogates I Loshchilov, M Schoenauer, M Sebag International Conference on Parallel Problem Solving from Nature, 364-373, 2010 | 127 | 2010 |
| Feature selection as a one-player game R Gaudel, M Sebag International Conference on Machine Learning, 359--366, 2010 | 120 | 2010 |
| Data stream clustering with affinity propagation X Zhang, C Furtlehner, C Germain-Renaud, M Sebag IEEE Transactions on Knowledge and Data Engineering 26 (7), 1644-1656, 2013 | 119 | 2013 |
| Data streaming with affinity propagation X Zhang, C Furtlehner, M Sebag Joint European Conference on Machine Learning and Knowledge Discovery in …, 2008 | 119 | 2008 |
Marc SchoenauerSenior Researcher Emeritus, INRIAVerified email at inria.fr
