APA
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Schein, A., Zhou, M., Blei, D. M., & Wallach, H. M. (2016). Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations. In Proceedings of the International Conference on Machine Learning (ICML).
Chicago/Turabian
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Schein, Aaron, Mingyuan Zhou, David M. Blei, and Hanna M. Wallach. “Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations.” In Proceedings of the International Conference on Machine Learning (ICML), 2016.
MLA
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Schein, Aaron, et al. “Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations.” Proceedings of the International Conference on Machine Learning (ICML), 2016.
BibTeX Click to copy
@inproceedings{aaron2016a,
title = {Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations},
year = {2016},
author = {Schein, Aaron and Zhou, Mingyuan and Blei, David M. and Wallach, Hanna M.},
booktitle = {Proceedings of the International Conference on Machine Learning (ICML)}
}
Abstract: We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country i took action a toward country j at time t." BPTD discovers overlapping country--community memberships, including the number of latent communities. In addition, it discovers directed community–community interaction networks that are specific to "topics" of action types and temporal "regimes." We show that BPTD yields an efficient MCMC inference algorithm and achieves better predictive performance than related models. We also demonstrate that it discovers interpretable latent structure that agrees with our knowledge of international relations.
