| Learning Optimal Decision Trees Using Caching Branch-and-Bound Search. G Aglin, S Nijssen, P Schaus AAAI, 3146-3153, 2020 | 128 | 2020 |
| PyDL8.5: a library for learning optimal decision trees G Aglin, S Nijssen, P Schaus Proceedings of the Twenty-Ninth International Conference on International …, 2021 | 28 | 2021 |
| Learning optimal decision trees under memory constraints G Aglin, S Nijssen, P Schaus Machine Learning and Knowledge Discovery in Databases: European Conference …, 2023 | 3 | 2023 |
| Generic Heuristic for the mnk-games AH Abdoulaye, VR Houndji, EC Ezin, G Aglin African Conference on Research in Computer Science (Stellenbosch, South …, 2018 | 3 | 2018 |
| Assessing Optimal Forests of Decision Trees G Aglin, S Nijssen, P Schaus 2021 IEEE 33rd International Conference on Tools with Artificial …, 2021 | 2 | 2021 |
| Interpretable Quantile Regression by Optimal Decision Trees V Lemaire, G Aglin, S Nijssen International Symposium on Intelligent Data Analysis, 210-222, 2024 | | 2024 |
| Efficient Lookahead Decision Trees H Kiossou, P Schaus, S Nijssen, G Aglin International Symposium on Intelligent Data Analysis, 133-144, 2024 | | 2024 |
| Optimal decision trees under constraints G Aglin UCL-Université Catholique de Louvain, 2022 | | 2022 |
| An Efficient Structured Perceptron for NP-hard Combinatorial Optimization Problems B Véjar, G Aglin, Aİ Mahmutoğulları, S Nijssen, P Schaus, T Guns | | |
| Heuristique générique pour les jeux de type «n-alignés» C Eugène, IVR HOUNDJI | | |
Pierre SchausICTEAM, EPL, UCLouvainDirección de correo verificada de uclouvain.be
