Aaron Schein


Postdoctoral Fellow at Columbia University


Curriculum vitae


aaron.schein@columbia.edu

Data Science Institute


Columbia University


New York, NY



Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts


Conference paper


Aaron Schein, John Paisley, David M. Blei, Hanna M. Wallach
Proceedings of the SIGKDD Conference of Knowledge Discovery and Data Mining (KDD), 2015


Other materials: [Code] [Poster] [Slides] (see below for video)
Figure 6: Julian Assange, editor-in-chief of WikiLeaks, sought asylum at the Ecuadorian embassy in the UK during June through August 2012.
Abstract: We present a Bayesian tensor factorization model for inferring latent group structures from dynamic pairwise interaction patterns. For decades, political scientists have collected and analyzed records of the form "country i took action a toward country j at time t"—known as dyadic events—in order to form and test theories of international relations. We represent these event data as a tensor of counts and develop Bayesian Poisson tensor factorization to infer a low-dimensional, interpretable representation of their salient patterns. We demonstrate that our model's predictive performance is better than that of standard non-negative tensor factorization methods. We also provide a comparison of our variational updates to their maximum likelihood counterparts. In doing so, we identify a better way to form point estimates of the latent factors than that typically used in Bayesian Poisson matrix factorization. Finally, we showcase our model as an exploratory analysis tool for political scientists. We show that the inferred latent factor matrices capture interpretable multilateral relations that both conform to and inform our knowledge of international affairs.

Cite

APA
Schein, A., Paisley, J., Blei, D. M., & Wallach, H. M. (2015). Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts. In Proceedings of the SIGKDD Conference of Knowledge Discovery and Data Mining (KDD).

Chicago/Turabian
Schein, Aaron, John Paisley, David M. Blei, and Hanna M. Wallach. “Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts.” In Proceedings of the SIGKDD Conference of Knowledge Discovery and Data Mining (KDD), 2015.

MLA
Schein, Aaron, et al. “Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts.” Proceedings of the SIGKDD Conference of Knowledge Discovery and Data Mining (KDD), 2015.