Daan Fierens

发表

Lifted Variable Elimination: A Novel Operator and Completeness Results pdf

Guy Van den Broeck, Hendrik Blockeel, Nima Taghipour, 2012, ArXiv.

ProbLog2: From probabilistic programming to statistical relational learning

Luc De Raedt, Guy Van den Broeck, Wannes Meert, 2012, NIPS 2012.

Pairwise Markov Logic

Kristian Kersting, Jesse Davis, Jian Chen, 2012, ILP.

Towards digesting the alphabet-soup of statistical relational learning

Luc De Raedt, Wannes Meert, Bart Demoen, 2008 .

Recent advances in lifted inference at Leuven

Luc De Raedt, Guy Van den Broeck, Wannes Meert, 2012 .

Context-Specific Independence in Directed Relational Probabilistic Models and its Influence on the Efficiency of Gibbs Sampling

Daan Fierens, Daan Fierens, 2010, ECAI.

Learning directed probabilistic logical models: ordering-search versus structure-search

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2007, Annals of Mathematics and Artificial Intelligence.

A Comparison of Pruning Criteria for Probability Trees

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2007 .

Domain and combining rules in Bayesian logic programs

Jan Ramon, Hendrik Blockeel, Daan Fierens, 2004 .

Predictive data mining in intensive care

Maurice Bruynooghe, Greet Van den Berghe, Hendrik Blockeel, 2006 .

The ACE Data Mining System User's Manual

Celine Vens, Jan Ramon, Hendrik Blockeel, 2009 .

Mining data from intensive care patients

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2007, Adv. Eng. Informatics.

On the Use of Combining Rules in Relational Probability Trees

Daan Fierens, 2010, SDM.

Three complementary approaches to context aware movie recommendation

Hendrik Blockeel, Daan Fierens, Hossein Rahmani, 2010, CAMRa '10.

On the Completeness of Lifted Variable Elimination

Guy Van den Broeck, Hendrik Blockeel, Nima Taghipour, 2013, StarAI@AAAI.

Learning directed probabilistic logical models from relational data

Daan Fierens, Daan Fierens, 2008, AI Commun..

A (further) comparison of approaches for learning probability trees

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2006 .

Inference and learning in probabilistic logic programs using weighted Boolean formulas

Luc De Raedt, Guy Van den Broeck, Dimitar Shterionov, 2013, Theory and Practice of Logic Programming.

Improving the Efficiency of Approximate Inference for Probabilistic Logical Models by means of Program Specialization pdf

Daan Fierens, 2011, ArXiv.

Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2007, ECML.

Biclustering of gene expression data using probabilistic logic learning

Hendrik Blockeel, Nima Taghipour, Daan Fierens, 2009 .

Lifted Variable Elimination: Decoupling the Operators from the Constraint Language pdf

Jesse Davis, Hendrik Blockeel, Nima Taghipour, 2013, J. Artif. Intell. Res..

Generalized Ordering-Search for Learning Directed Probabilistic Logical Models

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2006, ILP.

Constraints for probabilistic logic programming

Luc De Raedt, Guy Van den Broeck, Maurice Bruynooghe, 2012, NIPS 2012.

Instance-level accuracy versus bag-level accuracy in multi-instance learning

Hendrik Blockeel, Daan Fierens, Ó ViniciusTragantedo, 2011, Data Mining and Knowledge Discovery.

Machine learning methods for prediction in intensive care

Maurice Bruynooghe, Hendrik Blockeel, Daan Fierens, 2006 .

An exercise with statistical relational learning systems

Guy Van den Broeck, Wannes Meert, Maurice Bruynooghe, 2009 .

Logical Bayesian networks

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2004 .

A comparison of approaches for learning first-order logical probability estimation trees

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2005 .

Logical Bayesian Networks and Their Relation to Other Probabilistic Logical Models

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2005, BNAIC.

Lifted Variable Elimination with Arbitrary Constraints

Jesse Davis, Hendrik Blockeel, Nima Taghipour, 2012, AISTATS.

Lifted inference for probabilistic programming

Luc De Raedt, Guy Van den Broeck, Wannes Meert, 2012, NIPS 2012.

Improving the Efficiency of Gibbs Sampling for Probabilistic Logical Models by Means of Program Specialization

Daan Fierens, 2010, ICLP.

Learning Directed Probabilistic Logical Models Using Ordering-Search

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2007, ILP.

On the Relationship between Logical Bayesian Networks and Probabilistic Logic Programming Based on the Distribution Semantics

Daan Fierens, Daan Fierens, 2009, ILP.

Inference in Probabilistic Logic Programs using Weighted CNF's

Luc De Raedt, Guy Van den Broeck, Bernd Gutmann, 2011, UAI.

Completeness Results for Lifted Variable Elimination

Guy Van den Broeck, Hendrik Blockeel, Nima Taghipour, 2013, AISTATS.

A Comparison of Approaches for Learning Probability Trees

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2005, ECML.

Generalized ordering-search for learning directed probabilistic logical models

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2007, Machine Learning.

Learning the structure of directed probabilistic logical models

Daan Fierens, 2007 .

Probabilistic logical learning for biclustering: A case study with surprising results

Hendrik Blockeel, Nima Taghipour, Daan Fierens, 2010 .