2010 Poster Sessions : Directed Regression

Student Name : Yi-hao Kao
Advisor : Benjamin Van Roy
Research Areas: Information Systems
Abstract:
When used to guide decisions, linear regression analysis typically involves estimation of regression coefficients via ordinary least squares and their subsequent use to make decisions. When there are multiple response variables and features do not perfectly capture their relationships, it is beneficial to account for the decision objective when computing regression coefficients. Empirical optimization does so but sacrifices performance when features are well-chosen or training data are insufficient. We propose directed regression, an efficient algorithm that combines merits of ordinary least squares and empirical optimization. We demonstrate through a computational study that directed regression can generate significant performance gains over either alternative. We also develop a theory that motivates the algorithm.

Bio:
Yi-hao Kao is a PhD candidate advised by Professor Benjamin Van Roy in the Information Systems Lab of Electrical Engineering Department, Stanford University. He is broadly interested in machine learning and optimization, particularly the statistical methods that enhance decision quality. He received his B.S. in Electrical Engineering from National Taiwan University in 2006.