Monday, April 11, 2011
11:00 AM
Computer Studies Bldg. 209
Fei Sha
University of Southern California
Statistical Learning Algorithms for Discovering Hidden Structures in Data
Statistical modeling and inference of high-dimensional data is a common, yet challenging task in many areas. To address this important issue, exploiting hidden structures in data has become an increasingly appealing strategy. In this talk, I will describe two sets of work in that direction. I will start by describing how to identify low-dimensional structures which preserve probabilistic relations between random variables. To this end, we develop and apply techniques from nonparametric statistics to assert statistical (conditional) independences. I will then describe how to exploit sparse structures which lead to economical and interpretable probabilistic models. For this purpose, we investigate and propose sparsity-inducing regularization which result in sparse and low-rank solutions. Through out the talk, I will illustrate the utility of both types of structures with several application examples in visualization, pattern classification, exploratory data analysis, etc.

This talk is based on joint work with my students and other collaborators, under the support by NSF, DARPA and Google.

BIO: Fei Sha is an assistant professor at the Computer Science Department, U. of Southern California. He obtained his doctoral degree in 2007 from U. of Pennsylvania. Before joining USC in 2008, he spent some time at UC Berkeley as a postdoc and Yahoo Research as a research scientist. His primary research interest has been theory and application of statistical machine learning.

Refreshments will be provided at 10:45 AM