Publications of Daan Fierens

DBLP entry

Google Scholar entry

Microsoft Academic Search entry

Journal papers

  1. A comparison of pruning criteria for probability trees. D. Fierens, J. Ramon, H. Blockeel and M. Bruynooghe. Machine Learning, vol. 78(1-2), pp. 251-285, Springer, 2010. (impact factor publication year: 1.956) (impact factor most recent: 1.66)
    PDF     abstract     DOI: 10.1007/s10994-009-5147-1     online appendix    
  2. Generalized ordering-search for learning directed probabilistic logical models. J. Ramon, T. Croonenborghs, D. Fierens, H. Blockeel and M. Bruynooghe. Machine learning, vol. 70(2-3), pp. 169-188, Springer, 2008. (impact factor publication year: 2.326) (impact factor most recent: 1.66)
    PDF     abstract     DOI: 10.1007/s10994-007-5033-7    
  3. Learning directed probabilistic logical models: Ordering-search versus structure-search. D. Fierens, J. Ramon, M. Bruynooghe and H. Blockeel. Annals of Mathematics and Artificial Intelligence, vol. 54(1-3), pp. 99-133, Springer, 2008. (impact factor publication year: 0.722) (impact factor most recent: 0.89)
    PDF     abstract     DOI: 10.1007/s10472-009-9134-9    
  4. Learning directed probabilistic logical models from relational data. D. Fierens. AI Communications, vol. 21(4), pp. 269-270, IOS Press, 2008. (impact factor publication year: 0.608) (impact factor most recent: 0.76)
    PDF     abstract     DOI: 10.3233/AIC-2008-0428    
  5. Mining data from intensive care patients. J. Ramon, D. Fierens, F. Guiza Grandas, G. Meyfroidt, H. Blockeel, M. Bruynooghe and G. Van den Berghe. Advanced engineering informatics, vol. 21(3), pp. 243-256, Elsevier, 2007. (impact factor publication year: 1.172) (impact factor most recent: 1.73)
    PDF     abstract     DOI: 10.1016/j.aei.2006.12.002    
  6. Machine learning methods for prediction in intensive care. F. Guiza Grandas, J. Ramon, D. Fierens, G. Meyfroidt, H. Blockeel, M. Bruynooghe and G. Van den Berghe. Journal of Critical Care, vol. 21 (4), pp. 353-354, Elsevier, 2006. (impact factor publication year: 1.054) (impact factor most recent: 2.077)
    PDF     DOI: 10.1016/j.jcrc.2006.10.018    

Conference papers, published in proceedings

  1. Lifted variable elimination with arbitrary constraints. N. Taghipour, D. Fierens, J. Davis and H. Blockeel. In: Journal of Machine Learning Research - Proceedings Track vol.22, 15th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 1194-1202, April 2012. (acceptance rate: <33%)
    PDF     abstract    
  2. Instance-level accuracy versus bag-level accuracy in multi-instance learning. V. Tragante do O, D. Fierens and H. Blockeel. In: Proceedings of the 23rd Benelux Conference on Artificial Intelligence (BNAIC), November 2011.
    PDF     abstract    
  3. Inference in probabilistic logic programs using weighted CNFs. D. Fierens, G. Van den Broeck, I. Thon, B. Gutmann and L. De Raedt. In: Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), July 2011. (acceptance rate (plenary presentation): 8%)
    PDF     abstract    
  4. Context-specific independence in directed relational probabilistic models and its influence on the efficiency of Gibbs sampling. D. Fierens. In: Proceedings of the 19th European Conference on Artificial Intelligence (ECAI), pp. 243-248, IOS Press, August 2010. (acceptance rate: 22%)
    PDF     abstract    
  5. On the relationship between logical Bayesian networks and probabilistic logic programming based on the distribution semantics. D. Fierens. In: Postproceedings of the 19th International conference on Inductive Logic Programming (ILP), pp. 17-24, Springer Lecture Notes in Computer Science (vol.5989), July 2010.
    PDF     abstract     online appendix    
  6. Improving the efficiency of Gibbs sampling for probabilistic logical models by means of program specialization. D. Fierens. In: Technical Communications of 26th International Conference on Logic Programming (ICLP), pp. 74-83, Leibniz-Zentrum fuer Informatik - Schloss Dagstuhl, July 2010. (acceptance rate: 58%)
    PDF     abstract    
  7. On the use of combining rules in relational probability trees. D. Fierens. In: Proceedings of the 10th SIAM International Conference on Data Mining (SDM), pp. 397-408, Society for Industrial and Applied Mathematics, April/May 2010. (acceptance rate: 23%)
    PDF     abstract    
  8. On the relationship between logical Bayesian networks and probabilistic logic programming based on the distribution semantics. D. Fierens. In: Online proceedings of the 19th International Conference on Inductive Logic Programming (ILP), July 2009.
    PDF     abstract     online appendix    
  9. Learning directed probabilistic logical models using ordering-search. D. Fierens, J. Ramon, M. Bruynooghe and H. Blockeel. In: Postproceedings of the 17th International Conference on Inductive Logic Programming (ILP), pp. 24-24, Springer Lecture Notes in Computer Science (vol.4894), June 2008.
    PDF     abstract    
  10. Learning directed probabilistic logical models: Ordering-search versus structure-search. D. Fierens, J. Ramon, M. Bruynooghe and H. Blockeel. In: Proceedings of the 18th European Conference on Machine Learning (ECML), pp. 567-574, Springer Lecture Notes in Computer Science (vol.4701), September 2007. (acceptance rate: 24%)
    PDF     abstract    
  11. Generalized ordering-search for learning directed probabilistic logical models. J. Ramon, T. Croonenborghs, D. Fierens, H. Blockeel and M. Bruynooghe. In: Postproceedings of the 17th International Conference on Inductive Logic Programming (ILP), pp. 40-42, Springer Lecture Notes in Computer Science (vol.4455), August 2007.
    PDF     abstract    
  12. Generalizing orderingsearch for learning directed probabilistic logical models. J. Ramon, T. Croonenborghs, D. Fierens, H. Blockeel and M. Bruynooghe. In: Short Papers of the 16th International Conference on Inductive Logic Programming (ILP), pp. 173-175, August 2006.
    PDF     abstract    
  13. Predictive data mining in intensive care. F. Guiza Grandas, D. Fierens, J. Ramon, H. Blockeel, G. Meyfroidt, M. Bruynooghe and G. Van den Berghe. In: Proceedings of the 15th Annual Machine Learning Conference of Belgium and the Netherlands (BENELEARN), pp. 81-88, May 2006.
    PDF     abstract    
  14. A comparison of approaches for learning probability trees. D. Fierens, J. Ramon, H. Blockeel and M. Bruynooghe. In: Proceedings of the 16th European Conference on Machine Learning (ECML), pp. 556-563, Springer Lecture Notes in Computer Science (vol.3720), October 2005. (acceptance rate: 20%)
    PDF     abstract    
  15. A comparison of approaches for learning first-order logical probability estimation trees. D. Fierens, J. Ramon, H. Blockeel and M. Bruynooghe. In: Late-breaking papers of the 15th International Conference on Inductive Logic Programming (ILP), pp. 11-16, August 2005.
    PDF     abstract    
  16. Logical Bayesian networks and their relation to other probabilistic logical models. D. Fierens, H. Blockeel, M. Bruynooghe and J. Ramon. In: Proceedings of the 15th International Conference on Inductive Logic Programming (ILP), pp. 121-135, Springer Lecture Notes in Computer Science (vol.3625), August 2005. (acceptance rate: 51%)
    PDF     abstract    

Workshop papers

  1. Three complementary approaches to context aware movie recommendation. H. Blockeel, B. Piccart, H. Rahmani and D. Fierens. In: Proceedings of the Workshop on Context-Aware Movie Recommendation (CAMRa), pp. 57-60, ACM, September 2010.
    PDF     abstract    
  2. An exercise with statistical relational learning systems. M. Bruynooghe, B. De Cat, J. Drijkoningen, D. Fierens et. al. In: Proceedings of International Workshop on Statistical Relational Learning (SRL), July 2009.
    PDF     abstract    
  3. Logical Bayesian networks. D. Fierens, H. Blockeel, J. Ramon and M. Bruynooghe. In: Proceedings of the 3rd international workshop on Multi-Relational Data Mining (MRDM), pp. 19-30, August 2004.
    PDF     abstract    

Abstracts

  1. Biclustering of gene expression data using probabilistic logic learning. N. Taghipour, D. Fierens and H. Blockeel. Benelux Bioinformatics Conference (BBC), December 2009.
    abstract    
  2. Towards digesting the alphabet-soup of statistical relational learning. L. De Raedt, B. Demoen, D. Fierens et. al. NIPS 2008 Workshop Probabilistic Programming, December 2008.
    PDF    
  3. Generalized ordering-search for learning directed probabilistic logical models. J. Ramon, T. Croonenborghs, D. Fierens, H. Blockeel and M. Bruynooghe. The 31st Annual Conference of the German Classification Society on Data Analysis, Machine Learning, and Applications (GfKl), March 2007.
    PDF    
  4. Data mining for the prediction of intensive care unit (ICU) length of stay (LOS). G. Meyfroidt, F. Guiza Grandas, D. Fierens, J. Ramon and G. Van den Berghe. European Society of Intensive Care Medicine Meeting (ESICM), September 2006.
  5. A comparison of pruning criteria for probability trees. D. Fierens, H. Blockeel, J. Ramon and M. Bruynooghe. The 15th Annual Machine Learning Conference of Belgium and The Netherlands (BENELEARN), May 2006.
    abstract    
  6. Logical Bayesian networks and their relation to other probabilistic logical models. D. Fierens, H. Blockeel, M. Bruynooghe and J. Ramon. The 17th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC), October 2005.
    PDF    

Technical reports

  1. Improving the efficiency of approximate inference for probabilistic logical models by means of program specialization. D. Fierens. arXiv:1112.5381v1 [cs.AI]. 2011.
    PDF     abstract    
  2. Inference in probabilistic logic programs using weighted CNFs. D. Fierens, G. Van den Broeck, I. Thon, B. Gutmann and L. De Raedt. Department of Computer Science, Katholieke Universiteit Leuven, Report CW607. 2011.
    PDF     abstract    
  3. Probabilistic logical learning for biclustering: A case study with surprising results. N. Taghipour, D. Fierens and H. Blockeel. Department of Computer Science, Katholieke Universiteit Leuven, Report CW597. 2010.
    PDF     abstract    
  4. Improving the efficiency of Gibbs sampling for probabilistic logical models by means of program specialization. D. Fierens. Department of Computer Science, Katholieke Universiteit Leuven, Report CW581. 2010.
    PDF     abstract    
  5. Mapping logical Bayesian networks to probabilistic logic programs with distribution semantics. D. Fierens. Department of Computer Science, Katholieke Universiteit Leuven, Report CW563. 2009.
    PDF     abstract    
  6. Learning directed probabilistic logical models: Ordering-search versus structure-search. D. Fierens, J. Ramon, M. Bruynooghe and H. Blockeel. Department of Computer Science, Katholieke Universiteit Leuven, Report CW490. 2007.
    PDF     abstract    
  7. A comparison of pruning criteria for probability trees. D. Fierens, J. Ramon, H. Blockeel and M. Bruynooghe. Department of Computer Science, Katholieke Universiteit Leuven, Report CW488. 2007.
    PDF     abstract     online appendix    
  8. A comparison of approaches for learning probability trees. D. Fierens, J. Ramon, H. Blockeel and M. Bruynooghe. Department of Computer Science, Katholieke Universiteit Leuven, Report CW418. 2005.
    PDF     abstract    

Doctoral thesis

  1. Learning directed probabilistic logical models from relational data. D. Fierens. Katholieke Universiteit Leuven. July 2008.
    PDF     abstract    
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