Aaron Schein


Postdoctoral Fellow at Columbia University


Curriculum vitae


aaron.schein@columbia.edu

Data Science Institute


Columbia University


New York, NY



Poisson-Randomized Gamma Dynamical Systems


Conference paper


Aaron Schein, Scott LInderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach
Advances in Neural Information Processing Systems (NeurIPS), 2019


Other materials: [Code] [Poster]
Abstract: 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 burstiness. The PRGDS is based on a new motif in Bayesian latent variable modeling, an alternating chain of discrete Poisson and continuous gamma latent states that is analytically convenient and computationally tractable. This motif yields closed-form complete conditionals for all variables by way of the Bessel distribution and a novel discrete distribution that we call the shifted confluent hypergeometric distribution. We draw connections to closely related models and compare the PRGDS to these models in studies of real-world count data sets of text, international events, and neural spike trains. We find that a sparse variant of the PRGDS, which allows the continuous gamma latent states to take values of exactly zero, often obtains better predictive performance than other models and is uniquely capable of inferring latent structures that are highly localized in time.

Cite

APA
Schein, A., LInderman, S., Zhou, M., Blei, D. M., & Wallach, H. M. (2019). Poisson-Randomized Gamma Dynamical Systems. In Advances in Neural Information Processing Systems (NeurIPS).

Chicago/Turabian
Schein, Aaron, Scott LInderman, Mingyuan Zhou, David M. Blei, and Hanna M. Wallach. “Poisson-Randomized Gamma Dynamical Systems.” In Advances in Neural Information Processing Systems (NeurIPS), 2019.

MLA
Schein, Aaron, et al. “Poisson-Randomized Gamma Dynamical Systems.” Advances in Neural Information Processing Systems (NeurIPS), 2019.