Abstract: Models of sequential data such as the recurrent neural network (RNN) often implicitly treat a sequence as having a fixed time interval between observations and do not account for group-level effects when multiple sequences are observed. We propose a model for grouped sequential data based on the RNN that accounts for varying time intervals between observations in a sequence by learning a group-level parameter to which each sequence reverts as more time passes between observations. Our approach is motivated by the mixed membership framework, and can be used for dynamic topic modeling-type problems in which the distribution on topics (not the topics themselves) are evolving in time. We demonstrate our approach on two datasets: The Instacart set of 3.4 million online grocery orders made by 206K customers, and a UK retail set consisting of over 500K orders.

Bio: John Paisley is an Associate Professor in the Department of Electrical Engineering at Columbia University, where he is also a member of the Data Science Institute. He received his Ph.D. in Electrical and Computer Engineering from Duke University. His research interests include Bayesian modeling and inference techniques, Bayesian nonparametric methods, and their applications to a variety of machine learning problems.

http://www.columbia.edu/~jwp2128/