Events Calendar

CS Colloquium: John Lafferty (University of Chicago) - Statistical Learning Under Communication and Shape Constraints

Speaker: John Lafferty, University of Chicago

Talk Title: Statistical Learning Under Communication and Shape Constraints

Series: Yahoo! Labs Machine Learning Seminar Series

Abstract: Imagine that I estimate a statistical model from data, and then want to share my model with you. But we are communicating over a resource constrained channel. By sending lots of bits, I can communicate my model accurately, with little loss in statistical risk. Sending a small number of bits will incur some excess risk. What can we say about the tradeoff between statistical risk and the communication constraints? This is a type of rate distortion and constrained minimax problem, for which we provide a sharp analysis in certain nonparametric settings. We also consider the problem of estimating a high dimensional convex function, and develop a screening procedure to identify irrelevant variables. The approach adopts on a two-stage quadratic programming algorithm that estimates a sum of one-dimensional convex functions, beating the curse of dimensionality that holds under smoothness constraints. Joint work with Yuancheng Zhu and Min Xu.

Host: Yan Liu

Location: Ronald Tutor Hall of Engineering (RTH) - 526

Audiences: Everyone Is Invited

Contact: Assistant to CS chair