Aaron Schein


Assistant Professor of Stats & Data Science at UChicago


Curriculum vitae


[email protected]


Data Science Institute


University of Chicago


Chicago, IL



Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations


Conference paper


Aaron Schein, Mingyuan Zhou, David M. Blei, Hanna M. Wallach
Proceedings of the International Conference on Machine Learning (ICML), 2016

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APA
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
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
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.


Other materials: [Code] [Poster] [Slides]
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.

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