16 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…
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…
Denoising the Future: Top-p Distributions for Moving Through Time
Florian Andreas Marwitz, Ralf Möller, Magnus Bender +1
Inference in dynamic probabilistic models is a complex task involving expensive operations. In particular, for Hidden Markov Models, the whole state space has to be enumerated for…
Lifted Forward Planning in Relational Factored Markov Decision Processes with Concurrent Actions
Florian Andreas Marwitz, Tanya Braun, Ralf Möller +1
When allowing concurrent actions in Markov Decision Processes, whose state and action spaces grow exponentially in the number of objects, computing a policy becomes highly ineffici…