2 citations · 2 across the 21 of their papers we have counts for
4 papers · 1 filter
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…
Lifted Causal Inference
Malte Luttermann, Tanya Braun, Ralf Möller +1
Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering whil…
Inducing Comparability of Factorised Probability Distributions
Jan Speller, Malte Luttermann, Marcel Gehrke +1
To allow for principled comparison between two probabilistic graphical models defined over non-identical variable sets, they have to be lifted to a common measurable space. To this…
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…