• Aucun résultat trouvé

Symmetry in Probabilistic Databases

N/A
N/A
Info
Télécharger
Protected

Academic year: 2022

Partager "Symmetry in Probabilistic Databases"

Copied!
1
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

(1)

Symmetry in Probabilistic Databases

Guy Van den Broeck

Computer Science Department University of California, Los Angeles guyvdb@cs.ucla.edu

Abstract. Researchers in databases, AI, and machine learning, have all proposed representations of probability distributions over relational databases (possible worlds). In a tuple-independent probabilistic database, the possible worlds all have distinct probabilities, because the tuple prob- abilities are distinct. In AI and machine learning, however, one typically learns highly symmetric distributions, where large numbers of symmetric databases get assigned identical probability. This symmetry helps with generalizing from data. In this talk I discuss what happens to standard database notions of data and combined complexity when considering AI- style symmetric probabilistic databases. The question proves to be a fer- tile ground for database theory, with interesting connections to counting complexity and 0-1 laws.

Références

Télécharger maintenant ( PDF - 1 Page - 55.83 KB )

Documents relatifs

Skew-symmetric Nitsche's formulation in isogeometric analysis: Dirichlet and symmetry conditions, patch coupling and frictionless contact

A simple skew-symmetric Nitsche’s formulation is introduced into the framework of isogeometric analysis (IGA) to deal with various problems in small strain elasticity:

Text island spotting in large speech databases

(with exact transcript and a 10% WER transcripts) - Second experiments assessed on 11 hours of RTBF on wich time stamps where manually added. - All words available in database

Text island spotting in large speech databases

This method is evaluated both on the French ESTER ([4]) corpus and on a large database composed of records from the Radio Television Belge Francophone (RTBF) associated to

Use of Machine Learning for Anomaly Detection Problem in Large Astronomical Databases

Supernovae surveys detect hundreds of SNe candidates per year, but the lack of spec- troscopic information makes the processing algorithms to classify discovered SNe bas- ing

Provenance-Based Routing in Probabilistic Graph Databases

We gave an overview of a provenance-based framework for routing queries, and of di↵erent approaches considered to lower the complexity of query evaluation: considering the shape of

On Hamilton cycles in highly symmetric graphs

Consider the following sweeping generalization of the middle levels conjecture, which at the same time generalizes problems (i) and (ii) mentioned in Section 2: Can we generate

Lifted Inference in Probabilistic Databases

Al- though PDBs define a very simple probability space, by simply adding constraints one can model much richer probability spaces, such as those represented by Markov Logic Networks

Differential symmetry breaking operators: II. Rankin–Cohen operators for symmetric pairs

Key words and phrases: branching laws, Rankin–Cohen brackets, F-method, sym- metric pair, invariant theory, Verma modules, Hermitian symmetric spaces, Jacobi