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Generative Archimedean Copulas

Publication ,  Conference
Ng, Y; Hasan, A; Elkhalil, K; Tarokh, V
Published in: Proceedings of Machine Learning Research
January 1, 2021

We propose a new generative modeling technique for learning multidimensional cumulative distribution functions (CDFs) in the form of copulas. Specifically, we consider certain classes of copulas known as Archimedean and hierarchical Archimedean copulas, popular for their parsimonious representation and ability to model different tail dependencies. We consider their representation as mixture models with Laplace transforms of latent random variables from generative neural networks. This alternative representation allows for computational efficiencies and easy sampling, especially in high dimensions. We describe multiple methods for optimizing the network parameters. Finally, we present empirical results that demonstrate the efficacy of our proposed method in learning multidimensional CDFs and its computational efficiency compared to existing methods.

Duke Scholars

Published In

Proceedings of Machine Learning Research

EISSN

2640-3498

Publication Date

January 1, 2021

Volume

161

Start / End Page

643 / 653
 

Citation

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MLA
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Ng, Y., Hasan, A., Elkhalil, K., & Tarokh, V. (2021). Generative Archimedean Copulas. In Proceedings of Machine Learning Research (Vol. 161, pp. 643–653).
Ng, Y., A. Hasan, K. Elkhalil, and V. Tarokh. “Generative Archimedean Copulas.” In Proceedings of Machine Learning Research, 161:643–53, 2021.
Ng Y, Hasan A, Elkhalil K, Tarokh V. Generative Archimedean Copulas. In: Proceedings of Machine Learning Research. 2021. p. 643–53.
Ng, Y., et al. “Generative Archimedean Copulas.” Proceedings of Machine Learning Research, vol. 161, 2021, pp. 643–53.
Ng Y, Hasan A, Elkhalil K, Tarokh V. Generative Archimedean Copulas. Proceedings of Machine Learning Research. 2021. p. 643–653.

Published In

Proceedings of Machine Learning Research

EISSN

2640-3498

Publication Date

January 1, 2021

Volume

161

Start / End Page

643 / 653