Abstract
A generative probabilistic model for relational data
consists of a family of probability distributions
for relational structures over domains of different
sizes. In most existing statistical relational learn-
ing (SRL) frameworks, these models are not pro-
jective in the sense that the marginal of the distribu-
tion for size-n structures on induced substructures
of size k < n is equal to the given distribution for
size-k structures. Projectivity is very beneficial in
that it directly enables lifted inference and statis-
tically consistent learning from sub-sampled rela-
tional structures. In earlier work some simple frag-
ments of SRL languages have been identified that
represent projective models. However, no complete
characterization of, and representation framework
for projective models has been given. In this pa-
per we fill this gap: exploiting representation theo-
rems for infinite exchangeable arrays we introduce
a class of directed graphical latent variable models
that precisely correspond to the class of projective
relational models. As a by-product we also obtain a
characterization for when a given distribution over
size-k structures is the statistical frequency distri-
bution of size-k substructures in much larger size-
n structures. These results shed new light onto the
old open problem of how to apply Halpern et al.’s
“random worlds approach” for probabilistic infer-
ence to general relational signatures.
Originalsprog | Engelsk |
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Titel | Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) |
Forlag | International Joint Conferences on Artificial Intelligence |
Publikationsdato | 2020 |
Sider | 4283-4290 |
ISBN (Elektronisk) | 978-0-9992411-6-5 |
DOI | |
Status | Udgivet - 2020 |
Begivenhed | 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan Varighed: 1 jan. 2021 → … |
Konference
Konference | 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 |
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Land/Område | Japan |
By | Yokohama |
Periode | 01/01/2021 → … |
Sponsor | International Joint Conferences on Artifical Intelligence (IJCAI) |