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Found 8 papers, 3 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.
Efficient Detection of Exchangeable Factors in Factor Graphs
In particular, we introduce the detection of exchangeable factors (DEFT) algorithm, which allows us to drastically reduce the computational effort for checking whether two factors are exchangeable in practice.
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.
On the Completeness and Complexity of the Lifted Dynamic Junction Tree Algorithm
We contribute, to the best of our knowledge, the first completeness and complexity analysis for a temporal lifted algorithm, the so-called lifted dynamic junction tree algorithm (LDJT).
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.
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.
