| Inference and learning in probabilistic logic programs using weighted boolean formulas D Fierens, G Van den Broeck, J Renkens, D Shterionov, B Gutmann, ... Theory and Practice of Logic Programming 15 (3), 358-401, 2015 | 396 | 2015 |
| Mining data from intensive care patients J Ramon, D Fierens, F Güiza, G Meyfroidt, H Blockeel, M Bruynooghe, ... Advanced Engineering Informatics 21 (3), 243-256, 2007 | 116 | 2007 |
| Inference in probabilistic logic programs using weighted CNF's D Fierens, GV Broeck, I Thon, B Gutmann, L De Raedt arXiv preprint arXiv:1202.3719, 2012 | 106 | 2012 |
| Logical Bayesian networks and their relation to other probabilistic logical models D Fierens, H Blockeel, M Bruynooghe, J Ramon Inductive Logic Programming: 15th International Conference, ILP 2005, Bonn …, 2005 | 81 | 2005 |
| Lifted variable elimination: Decoupling the operators from the constraint language N Taghipour, D Fierens, J Davis, H Blockeel Journal of Artificial Intelligence Research 47, 393-439, 2013 | 78 | 2013 |
| Towards digesting the alphabet-soup of statistical relational learning L De Raedt, B Demoen, D Fierens, B Gutmann, G Janssens, A Kimmig, ... NIPS* 2008 Workshop Probabilistic Programming, Date: 2008/12/13-2008/12/13 …, 2008 | 58 | 2008 |
| Instance-level accuracy versus bag-level accuracy in multi-instance learning G Vanwinckelen, V Tragante Do O, D Fierens, H Blockeel Data mining and knowledge discovery 30, 313-341, 2016 | 38 | 2016 |
| Completeness results for lifted variable elimination N Taghipour, D Fierens, G Van den Broeck, J Davis, H Blockeel Artificial Intelligence and Statistics, 572-580, 2013 | 33 | 2013 |
| Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens, and Luc De Raedt. Inference and learning in probabilistic logic programs using weighted boolean formulas D Fierens, G Van den Broeck, J Renkens, D Sht Theory Pract. Log. Program 15 (3), 358-401, 2015 | 32 | 2015 |
| Lifted variable elimination with arbitrary constraints N Taghipour, D Fierens, J Davis, H Blockeel Artificial Intelligence and Statistics, 1194-1202, 2012 | 30 | 2012 |
| Constraints for probabilistic logic programming D Fierens, G Van den Broeck, M Bruynooghe, L De Raedt Proceedings of the NIPS probabilistic programming workshop 2, 129-174, 2012 | 22 | 2012 |
| The ace data mining system, user’s manual H Blockeel, L Dehaspe, J Ramon, J Struyf, A Van Assche, C Vens, ... Katholieke Universiteit Leuven, Belgium, 2006 | 19 | 2006 |
| A comparison of approaches for learning probability trees D Fierens, J Ramon, H Blockeel, M Bruynooghe Machine Learning: ECML 2005: 16th European Conference on Machine Learning …, 2005 | 19 | 2005 |
| Logical bayesian networks D Fierens, H Blockeel, J Ramon, M Bruynooghe Third workshop on multi-relational data mining, 19-30, 2004 | 18 | 2004 |
| A comparison of pruning criteria for probability trees D Fierens, J Ramon, H Blockeel, M Bruynooghe Machine Learning 78, 251-285, 2010 | 17 | 2010 |
| Generalized ordering-search for learning directed probabilistic logical models J Ramon, T Croonenborghs, D Fierens, H Blockeel, M Bruynooghe Machine Learning 70, 169-188, 2008 | 16 | 2008 |
| ProbLog2: From probabilistic programming to statistical relational learning J Renkens, D Shterionov, G Van den Broeck, J Vlasselaer, D Fierens, ... Proc. of NIPS, 1-5, 2012 | 15 | 2012 |
| Three complementary approaches to context aware movie recommendation H Rahmani, B Piccart, D Fierens, H Blockeel Proceedings of the Workshop on Context-Aware Movie Recommendation, 57-60, 2010 | 15 | 2010 |
| Instance-level accuracy versus bag-level accuracy in multi-instance learning V Tragante do O, D Fierens, H Blockeel Proceedings of the 23rd Benelux conference on artificial intelligence (BNAIC), 8, 2011 | 10 | 2011 |
| Context-specific independence in directed relational probabilistic models and its influence on the efficiency of Gibbs sampling D Fierens ECAI 2010, 243-248, 2010 | 10 | 2010 |
Hendrik BlockeelDepartment of Computer Science, KU LeuvenVerified email at cs.kuleuven.be
