Relational learning with statistical predicate invention: better models for hypertext
Machine Learning - Special issue on inducive logic programming
Database Management Systems
Constraint Processing
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Lifted first-order probabilistic inference
Lifted first-order probabilistic inference
Lifted probabilistic inference with counting formulas
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Lifted first-order belief propagation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
PrDB: managing and exploiting rich correlations in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Lifted aggregation in directed first-order probabilistic models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Probabilistic inductive logic programming: theory and applications
Probabilistic inductive logic programming: theory and applications
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Constraint processing in lifted probabilistic inference
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Bisimulation-based approximate lifted inference
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
An integrated approach to learning bayesian networks of rules
ECML'05 Proceedings of the 16th European conference on Machine Learning
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Lifted probabilistic inference by first-order knowledge compilation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Hi-index | 0.00 |
Lifted probabilistic inference algorithms exploit regularities in the structure of graphical models to perform inference more efficiently. More specifically, they identify groups of interchangeable variables and perform inference once per group, as opposed to once per variable. The groups are defined by means of constraints, so the flexibility of the grouping is determined by the expressivity of the constraint language. Existing approaches for exact lifted inference use specific languages for (in)equality constraints, which often have limited expressivity. In this article, we decouple lifted inference from the constraint language. 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. As a result, lifted inference can be performed using more powerful constraint languages, which provide more opportunities for lifting. We empirically demonstrate that this can improve inference efficiency by orders of magnitude, allowing exact inference where until now only approximate inference was feasible.