Daan Fierens

Publications
Influence

Inference and learning in probabilistic logic programs using weighted Boolean formulas

Daan Fierens, Guy Van den Broeck, +5 authors L. D. Raedt
Computer Science
Theory Pract. Log. Program.
25 April 2013

This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs in which some of the facts are annotated with probabilities. Expand

Lifted Variable Elimination: Decoupling the Operators from the Constraint Language

Nima Taghipour, Daan Fierens, J. Davis, Hendrik Blockeel
Mathematics, Computer Science
J. Artif. Intell. Res.
1 May 2013

We define operators for lifted inference in terms of relational algebra operators, so that they operate on the semantic level (the constraints' extension) rather than on the syntactic level, making them language-independent. Expand

Inference in Probabilistic Logic Programs using Weighted CNF's

Daan Fierens, Guy Van den Broeck, Ingo Thon, B. Gutmann, L. D. Raedt
Mathematics, Computer Science
UAI
14 July 2011

We improve upon the state-of-the-art in probabilistic logic programming by developing efficient inference algorithms for MAP and MAP inference tasks. Expand

Logical Bayesian Networks and Their Relation to Other Probabilistic Logical Models

Daan Fierens, Hendrik Blockeel, M. Bruynooghe, J. Ramon
Computer Science
BNAIC
10 August 2005

We introduce Logical Bayesian Networks (LBNs) as another language for knowledge based model construction of Bayesian networks, besides existing languages such as Probabilistic Relational Models and Bayesian Logic Programs. Expand

Predictive data mining in intensive care

F. G. Grandas, Daan Fierens, +4 authors G. Berghe
Computer Science
1 May 2006

In this paper we describe an application of data mining methods in an ICU database for different prediction tasks in an intensive care unit. Expand

Mining data from intensive care patients

J. Ramon, Daan Fierens, +4 authors G. V. D. Berghe
Computer Science
Adv. Eng. Informatics
1 July 2007

In this paper we describe the application of data mining methods for predicting the evolution of patients in an intensive care unit. Expand

Towards digesting the alphabet-soup of statistical relational learning

L. D. Raedt, Bart Demoen, +10 authors Joost Vennekens
Computer Science
1 December 2008

This paper reports on our work towards the development of a probabilistic logic programming environment intended as a target language in which other Probabilistic languages can be compiled, thereby contributing to the digestion of the “alphabet soup”. Expand

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

Gitte Vanwinckelen, Ó. ViniciusTragantedo, Daan Fierens, Hendrik Blockeel
Computer Science
Data Mining and Knowledge Discovery
1 November 2011

In multi-instance learning, instances are organized into bags, and a bag is labeled positive if it contains at least one positive instance, and negative otherwise; the labels of individual instances are not given. Expand

Logical Bayesian networks

Daan Fierens, Hendrik Blockeel, J. Ramon, M. Bruynooghe
Computer Science
1 August 2004

Lifted Variable Elimination with Arbitrary Constraints

Nima Taghipour, Daan Fierens, J. Davis, Hendrik Blockeel
Mathematics, Computer Science
AISTATS
21 March 2012

Lifted probabilistic inference algorithms exploit regularities in the structure of graphical models to perform inference more eciently. Expand

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