2 citations · 4 across the 20 of their papers we have counts for
6 papers · 1 filter
On Probabilistic Inference Through Parametric Tensor Decomposition in Base Tensor Networks
Sagad Hamid, Tanya Braun
Probabilistic inference is generally only tractable in low-treewidth graphical models, limiting its effective applicability in high-treewidth settings. Many existing methods improv…
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…
The Curious Case of Exploding DecPOMDPs: Containing the Fire through Policy Counting
Nazlı Nur Karabulut, Tanya Braun
Decentralised partially observable Markov decision processes (DecPOMDPs) provide a general framework for modelling multi-agent decision making under uncertainty. However, DecPOMDPs…
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…
A First Step Towards Even More Sparse Encodings of Probability Distributions
Florian Andreas Marwitz, Tanya Braun, Ralf Möller
Real world scenarios can be captured with lifted probability distributions. However, distributions are usually encoded in a table or list, requiring an exponential number of values…