Back to Departmental Colloquium: Spring 2016
Departmental Colloquium
Date: Tuesday, Feb 9, 2016
Time: 4:00PM
Location: LCB 219
*Tuesday*
Lizhen Lin
University of Texas at Austin
Title |
Robust and Scalable Inference for Big Data |
Abstract |
While theoretically justified and computationally efficient point estimators were developed in robust estimation for many problems, robust Bayesian analogues are not sufficiently well-understood. We propose a novel approach to Bayesian analysis that is provably robust to the presence of outliers and contaminations in the data, and is computationally scalable to big data. Our approach is based on the idea of splitting the data into several non-overlapping subsets, evaluating the posterior distribution given each subset data, and then combining the resulting subset posterior measures by taking the geometric medians. The resulting final measure is called the median posterior which is the ultimate object used for inference. We show several strong theoretical results for the median posterior, including concentration rates and provable robustness. We illustrate and validate the method through experiments on simulated and real data. [Joint work with Stas Minker, Sanvesh Srivastava and David Dunson] |
Applied Mathematics