• Corpus ID: 236429079

Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption

@article{Antonucci2021StructuralLO,
  title={Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption},
  author={Alessandro Antonucci and Alessandro Facchini and Lilith Mattei},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.12130}
}
Probabilistic sentential decision diagrams are a class of structured-decomposable probabilistic circuits especially designed to embed logical constraints. To adapt the classical LEARNSPN scheme to learn the structure of these models, we propose a new scheme based on a partial closed-world assumption: data implicitly provide the logical base of the circuit. Sum nodes are thus learned by recursively clustering batches in the initial data base, while the partitioning of the variables obeys a given… 

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References

SHOWING 1-10 OF 17 REFERENCES
Learning the Structure of Probabilistic Sentential Decision Diagrams
TLDR
The first PSDD structure learning algorithm, called LEARNPSDD, is developed and retains the ability to learn PSDD structures in probability spaces subject to logical constraints, which is beyond the reach of other representations.
Probabilistic Sentential Decision Diagrams
TLDR
It is shown how the parameters of a PSDD can be efficiently estimated, in closed form, from complete data, and empirically evaluate the quality of PS-DDs learned from data, when the authors have knowledge, a priori, of the domain logical constraints.
Juice: A Julia Package for Logic and Probabilistic Circuits
TLDR
By leveraging parallelism (on both CPU and GPU), JUICE provides a fast implementation of circuit-based algorithms, which makes it suitable for tackling large-scale datasets and models.
Learning Arithmetic Circuits
TLDR
This work learns arithmetic circuits with a penalty on the number of edges in the circuit (in which the cost of inference is linear) that is equivalent to learning a Bayesian network with context-specific independence by greedily splitting conditional distributions.
Learning the Structure of Sum-Product Networks
TLDR
This work proposes the first algorithm for learning the structure of SPNs that takes full advantage of their expressiveness, and shows that the learned SPNs are typically comparable to graphical models in likelihood but superior in inference speed and accuracy.
SDD: A New Canonical Representation of Propositional Knowledge Bases
We identify a new representation of propositional knowledge bases, the Sentential Decision Diagram (SDD), which is interesting for a number of reasons. First, it is canonical in the presence of
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
TLDR
This paper proposes EiNets, a novel implementation design for PCs that combines a large number of arithmetic operations in a single monolithic einsum-operation, leading to speedups and memory savings of up to two orders of magnitude, in comparison to previous implementations.
On Closed World Data Bases
  • R. Reiter
  • Computer Science
    Logic and Data Bases
  • 1977
TLDR
This paper shows that closed world evaluation of an arbitrary query may be reduced to open world evaluated of so-called atomic queries, and shows that the closed world assumption can lead to inconsistencies, but for Horn data bases no such inconsistencies can arise.
Knowledge vault: a web-scale approach to probabilistic knowledge fusion
TLDR
The Knowledge Vault is a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories that computes calibrated probabilities of fact correctness.
Incomplete Information in Relational Databases
TLDR
There are precise conditions that should be satisfied in a semantically meaningful extension of the usual relational operators, such as projection, selection, union, and join, from operators on relations to operators on tables with “null values” of various kinds allowed.
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