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Daan Fierens
Daan Fierens
Department of Computer Science, KULeuven
Verified email at cs.kuleuven.be
Title
Cited by
Cited by
Year
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
3172015
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
1132007
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
912012
Logical Bayesian networks and their relation to other probabilistic logical models
D Fierens, H Blockeel, M Bruynooghe, J Ramon
International Conference on Inductive Logic Programming, 121-135, 2005
862005
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
582013
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
552008
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 (2), 313-341, 2016
292016
Lifted variable elimination with arbitrary constraints
N Taghipour, D Fierens, J Davis, H Blockeel
Artificial Intelligence and Statistics, 1194-1202, 2012
282012
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 and Practice of Logic Programming 15 (3), 358-401, 2015
272015
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
262013
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
212006
A comparison of approaches for learning probability trees
D Fierens, J Ramon, H Blockeel, M Bruynooghe
European Conference on Machine Learning, 556-563, 2005
212005
Constraints for probabilistic logic programming
D Fierens, G Van den Broeck, M Bruynooghe, L De Raedt
Proceedings of the NIPS probabilistic programming workshop, 1-4, 2012
202012
Logical bayesian networks
D Fierens, H Blockeel, J Ramon, M Bruynooghe
Proceedings of the 3rd international workshop on multi-relational data …, 2004
192004
Generalized ordering-search for learning directed probabilistic logical models
J Ramon, T Croonenborghs, D Fierens, H Blockeel, M Bruynooghe
Machine Learning 70 (2), 169-188, 2008
182008
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
162010
A comparison of pruning criteria for probability trees
D Fierens, J Ramon, H Blockeel, M Bruynooghe
Machine Learning 78 (1), 251-285, 2010
152010
ProbLog2: From probabilistic programming to statistical relational learning
J Renkens, D Shterionov, G Van den Broeck, J Vlasselaer, D Fierens, ...
Proceedings of the NIPS Probabilistic Programming Workshop, 2012
142012
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
102011
On the relationship between logical bayesian networks and probabilistic logic programming based on the distribution semantics
D Fierens
International Conference on Inductive Logic Programming, 17-24, 2009
92009
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Articles 1–20