2 citations · 4 across the 20 of their papers we have counts for
21 papers
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…
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…
Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs
Malte Luttermann, Tanya Braun, Ralf Möller +1
Lifting uses a representative of indistinguishable individuals to exploit symmetries in probabilistic relational models, denoted as parametric factor graphs, to speed up inference…
Enhancement of Subjective Content Descriptions by using Human Feedback
Magnus Bender, Tanya Braun, Ralf Möller +1
An agent providing an information retrieval service may work with a corpus of text documents. The documents in the corpus may contain annotations such as Subjective Content Descrip…
Lifted Causal Inference in Relational Domains
Malte Luttermann, Mattis Hartwig, Tanya Braun +2
Lifted inference exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintainin…
Colour Passing Revisited: Lifted Model Construction with Commutative Factors
Malte Luttermann, Tanya Braun, Ralf Möller +1
Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes. To apply lifted inference,…
DPM: Clustering Sensitive Data through Separation
Johannes Liebenow, Yara Schütt, Tanya Braun +3
Clustering is an important tool for data exploration where the goal is to subdivide a data set into disjoint clusters that fit well into the underlying data structure. When dealing…
Lifting DecPOMDPs for Nanoscale Systems -- A Work in Progress
Tanya Braun, Stefan Fischer, Florian Lau +1
DNA-based nanonetworks have a wide range of promising use cases, especially in the field of medicine. With a large set of agents, a partially observable stochastic environment, and…
Exploring Unknown Universes in Probabilistic Relational Models
Tanya Braun, Ralf Möller
Large probabilistic models are often shaped by a pool of known individuals (a universe) and relations between them. Lifted inference algorithms handle sets of known individuals for…
Taming Reasoning in Temporal Probabilistic Relational Models
Marcel Gehrke, Ralf Möller, Tanya Braun
Evidence often grounds temporal probabilistic relational models over time, which makes reasoning infeasible. To counteract groundings over time and to keep reasoning polynomial by…
Answering Hindsight Queries with Lifted Dynamic Junction Trees
Marcel Gehrke, Tanya Braun, Ralf Möller
The lifted dynamic junction tree algorithm (LDJT) efficiently answers filtering and prediction queries for probabilistic relational temporal models by building and then reusing a f…
Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
Marcel Gehrke, Tanya Braun, Ralf Möller
The lifted dynamic junction tree algorithm (LDJT) efficiently answers filtering and prediction queries for probabilistic relational temporal models by building and then reusing a f…
Fusing First-order Knowledge Compilation and the Lifted Junction Tree Algorithm
Tanya Braun, Ralf Möller
Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries as well as first-order knowle…