Jacob Bien is an assistant professor of data sciences and operations at the University of Southern California-Marshall. Dr. Bien's research focuses on statistical machine learning and in particular the development of novel methods that balance flexibility and interpretability for analyzing complex data. He combines ideas from convex optimization and statistics to develop methods that are of direct use to scientists and others with large datasets. His work has been supported by an NSF CAREER award and a three-year NSF grant on high-dimensional covariance estimation. He serves as an associate editor of Biometrika and Biostatistics. Before joining USC, he was an assistant professor at Cornell.
Talk: High-Dimensional Variable Selection When Features are Sparse
Abstract: It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events. This leads to design matrices in which a large number of columns are highly sparse. The challenge posed by such "rare features" has received little attention despite its prevalence in diverse areas, ranging from biology (e.g., rare species) to natural language processing (e.g., rare words). We show, both theoretically and empirically, that not explicitly accounting for the rareness of features can greatly reduce the effectiveness of an analysis. We next propose a framework for aggregating rare features into denser features in a flexible manner that creates better predictors of the response. An application to online hotel reviews demonstrates the gain in accuracy achievable by proper treatment of rare words. This is joint work with Xiaohan Yan.