Showing cs.AIShow all
3 papers · 1 filter
cs.AI2025
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…
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…