Ben Van Roy, Stanford University

*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************

Event Details
  • Date/Time:
    • Tuesday February 9, 2010 - Wednesday February 10, 2010
      10:00 am - 10:59 am
  • Location: TBD
  • Phone:
  • URL:
  • Email:
  • Fee(s):
    N/A
  • Extras:
Contact

Ton Dieker, ISyE
Contact Ton Dieker
404-385-3140

Summaries

Summary Sentence: Directed Regression

Full Summary: When used to guide decisions, linear regression analysis typically involves estimation of regression coefficients via ordinary least squares and their subsequent use in an optimization problem. When features are not chosen perfectly, it can be 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 computational studies that directed regression generates performance gains over either alternative. We also develop a theory that motivates the algorithm.

Speaker
Ben Van Roy
Stanford University

Abstract
When used to guide decisions, linear regression analysis typically involves estimation of regression coefficients via ordinary least squares and their subsequent use in an optimization problem. When features are not chosen perfectly, it can be 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 computational studies that directed regression generates performance gains over either alternative. We also develop a theory that motivates the algorithm.

Additional Information

In Campus Calendar
No
Groups

School of Industrial and Systems Engineering (ISYE)

Invited Audience
No audiences were selected.
Categories
Seminar/Lecture/Colloquium
Keywords
No keywords were submitted.
Status
  • Created By: Mike Alberghini
  • Workflow Status: Published
  • Created On: Dec 20, 2012 - 11:07am
  • Last Updated: Oct 7, 2016 - 10:01pm