2 citations · 4 across the 5 of their papers we have counts for
13 papers · 1 filter
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…
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…
Compression versus Accuracy: A Hierarchy of Lifted Models
Jan Speller, Malte Luttermann, Marcel Gehrke +1
Probabilistic graphical models that encode indistinguishable objects and relations among them use first-order logic constructs to compress a propositional factorised model for more…
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…
Approximate Lifted Model Construction
Malte Luttermann, Jan Speller, Marcel Gehrke +3
Probabilistic relational models such as parametric factor graphs enable efficient (lifted) inference by exploiting the indistinguishability of objects. In lifted inference, a repre…
Combining Local Symmetry Exploitation and Reinforcement Learning for Optimised Probabilistic Inference -- A Work In Progress
Sagad Hamid, Tanya Braun
Efficient probabilistic inference by variable elimination in graphical models requires an optimal elimination order. However, finding an optimal order is a challenging combinatoria…