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cs.AI2024
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…
cs.AI2024
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…
cs.AI2024
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…