758 citations · 1.8k across the 31 of their papers we have counts for
8 papers · 1 filter
Doubly Non-Central Beta Matrix Factorization for DNA Methylation Data
Aaron Schein, Anjali Nagulpally, Hanna Wallach +1
We present a new non-negative matrix factorization model for bounded-support data based on the doubly non-central beta (DNCB) distribution, a generalization of the beta dis…
Poisson-Randomized Gamma Dynamical Systems
Aaron Schein, Scott W. Linderman, Mingyuan Zhou +2
This paper presents the Poisson-randomized gamma dynamical system (PRGDS), a model for sequentially observed count tensors that encodes a strong inductive bias toward sparsity and…
Locally Private Bayesian Inference for Count Models
Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield +2
We present a general method for privacy-preserving Bayesian inference in Poisson factorization, a broad class of models that includes some of the most widely used models in the soc…
Poisson--Gamma Dynamical Systems
Aaron Schein, Mingyuan Zhou, Hanna Wallach
We introduce a new dynamical system for sequentially observed multivariate count data. This model is based on the gamma--Poisson construction---a natural choice for count data---an…
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
Aaron Schein, Mingyuan Zhou, David M. Blei +1
We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country to…
Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts
Aaron Schein, John Paisley, David M. Blei +1
We present a Bayesian tensor factorization model for inferring latent group structures from dynamic pairwise interaction patterns. For decades, political scientists have collected…
The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation
Fangjian Guo, Charles Blundell, Hanna Wallach +1
We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people's language usage over time, this model discov…
Learning the Structure of Deep Sparse Graphical Models
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghahramani
Deep belief networks are a powerful way to model complex probability distributions. However, learning the structure of a belief network, particularly one with hidden units, is diff…