6 papers
Lifted Model Construction under Approximate Commutativity
Malte Luttermann, Jan Speller, Tanya Braun +2
Lifted inference algorithms enable scalable probabilistic inference even for large object domains by leveraging the indistinguishability of objects in a probability distribution. A…
On the Detection of Commutative Factors in Factor Graphs: Necessary and Sufficient Conditions
Malte Luttermann, Ralf Möller, Marcel Gehrke
Exploiting the indistinguishability of objects in a probabilistic graphical model such as a factor graph is key to lifted probabilistic inference algorithms and allows for tractabl…
Lifting Factor Graphs with Some Unknown Factors for New Individuals
Malte Luttermann, Ralf Möller, Marcel Gehrke
Lifting exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, allowing to carry out query answering more efficiently while…
Lifted Model Construction without Normalisation: A Vectorised Approach to Exploit Symmetries in Factor Graphs
Malte Luttermann, Ralf Möller, Marcel Gehrke
Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes of logical variables. We fou…
Towards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models
Malte Luttermann, Ralf Möller, Mattis Hartwig
Probabilistic relational models provide a well-established formalism to combine first-order logic and probabilistic models, thereby allowing to represent relationships between obje…
Lifting Factor Graphs with Some Unknown Factors
Malte Luttermann, Ralf Möller, Marcel Gehrke
Lifting exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, allowing to carry out query answering more efficiently while…