Seminar Series - Lucas Janson, PhD

November 2, 2018 - 2:00am to 3:00am

The Seminar Series Presents:

Lucas Janson, PhD

Professor Janson is an Assistant Professor in the Department of Statistics at Harvard University, where he works on high-dimensional inference, autonomous robotic motion planning, and statistical machine learning. Prior to Harvard, he was a PhD student in Statistics at Stanford University advised by Professor Emmanuel Candès.

Dr. Janson will present "Should We Model X in High-Dimensional Inference" on Friday, November 2, 2018.

Abstract: For answering questions about the relationship between a response variable Y and a set of explanatory variables X, most statistical methods focus their assumptions on the conditional distribution of Y given X (or Y | X for short). I will describe some benefits of shifting those assumptions from the conditional distribution Y | X to the joint distribution of X, especially for high-dimensional data. First, modeling X can lead to assumptions that are more realistic and verifiable. Second, there are substantial methodological payoffs in terms of much greater flexibility in the tools an analyst can bring to bear on their data while also being guaranteed exact (non-asymptotic) inference. I will briefly mention some of my recent and ongoing work on methods for high-dimensional inference that model X instead of Y, as well as some challenges and interesting directions for the future.

Location and Address

1811 Wesley W. Posvar Hall

230 S. Bouquet St.

Pittsburgh, PA 15260