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Found 9 papers, 2 papers with code
Lifted Causal Inference in Relational Domains
Lifted inference exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers.
Colour Passing Revisited: Lifted Model Construction with Commutative Factors
Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes.
DPM: Clustering Sensitive Data through Separation
Privacy-preserving clustering groups data points in an unsupervised manner whilst ensuring that sensitive information remains protected.
Lifting DecPOMDPs for Nanoscale Systems -- A Work in Progress
DNA-based nanonetworks have a wide range of promising use cases, especially in the field of medicine.
Exploring Unknown Universes in Probabilistic Relational Models
Large probabilistic models are often shaped by a pool of known individuals (a universe) and relations between them.
Taming Reasoning in Temporal Probabilistic Relational Models
Evidence often grounds temporal probabilistic relational models over time, which makes reasoning infeasible.
Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
The lifted dynamic junction tree algorithm (LDJT) efficiently answers filtering and prediction queries for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps.
Fusing First-order Knowledge Compilation and the Lifted Junction Tree Algorithm
Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries as well as first-order knowledge compilation (FOKC) based on weighted model counting.
Answering Hindsight Queries with Lifted Dynamic Junction Trees
The lifted dynamic junction tree algorithm (LDJT) efficiently answers filtering and prediction queries for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps.
