Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of
activity, with many proposals for languages and algorithms for inference and learning. This …

Lifted graphical models provide a language for expressing dependencies between different
types of entities, their attributes, and their diverse relations, as well as techniques for …

We look at probabilistic first-order formalisms where the domain objects are known. In these
formalisms, the standard approach for inference is lifted variable elimination. To benefit from …

We consider the problem of weighted first-order model counting (WFOMC): given a first-
order sentence ϕ and domain size n∈ N, determine the weighted sum of models of ϕ over …

Probabilistic models involving relational and temporal aspects need exact and efficient
inference algorithms. Existing approaches are approximative, include unnecessary …

Given a first-order sentence? and a domain size n, how can one sample a model of? on the
domain {1,..., n} efficiently as n scales? We consider two variants of this problem: the uniform …

Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for
tractable probabilistic inference with respect to domain sizes. To apply lifted inference, a …

Privacy-preserving inference aims to avoid revealing identifying information about
individuals during inference. Lifted probabilistic inference works with groups of …

We consider the weighted first-order model counting (WFOMC) task, a problem with
important applications to inference and learning in structured graphical models. Bringing …

Lifted probabilistic inference exploits symmetries in probabilistic graphical models to allow
for tractable probabilistic inference with respect to domain sizes. To exploit symmetries in …