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Proceedings Paper

Learning vine copula models for synthetic data generation

Publicated to:33rd Aaai Conference On Artificial Intelligence, Aaai 2019, 31st Innovative Applications Of Artificial Intelligence Conference, Iaai 2019 And The 9th Aaai Symposium On Educational Advances In Artificial Intelligence, Eaai 2019. 5049-5057 - 2019-01-01 (), DOI: 10.1609/aaai.v33i01.33015049

Authors: Sun Y; Cuesta-Infante A; Veeramachaneni K

Affiliations

MIT - Author
Universidad Rey Juan Carlos, Spain - Author

Abstract

A vine copula model is a flexible high-dimensional dependence model which uses only bivariate building blocks. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development. In this work, we formulate a vine structure learning problem with both vector and reinforcement learning representation. We use neural network to find the embeddings for the best possible vine model and generate a structure. Throughout experiments on synthetic and real-world datasets, we show that our proposed approach fits the data better in terms of log-likelihood. Moreover, we demonstrate that the model is able to generate high-quality samples in a variety of applications, making it a good candidate for synthetic data generation. Copyright © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Keywords
Artificial intelligenceBuilding blockesDependence modelHigh-dimensionalLog likelihoodModel selectionReal-world datasetsReinforcement learningStructure-learningSynthetic data generations

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 8.21, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions May 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-05-15, the following number of citations:

  • Scopus: 30
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-05-15:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 66 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.