Aaron Schein


Postdoctoral Fellow at Columbia University


Curriculum vitae


aaron.schein@columbia.edu

Data Science Institute


Columbia University


New York, NY



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


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.

Cite

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.