Learning Vine Copula Models for Synthetic Data Generation


  • Yi Sun Massachusetts Institute of Technology
  • Alfredo Cuesta-Infante Universidad Rey Juan Carlos
  • Kalyan Veeramachaneni Massachusetts Institute of Technology




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 loglikelihood. 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.




How to Cite

Sun, Y., Cuesta-Infante, A., & Veeramachaneni, K. (2019). Learning Vine Copula Models for Synthetic Data Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 5049-5057. https://doi.org/10.1609/aaai.v33i01.33015049



AAAI Technical Track: Machine Learning