Statistical Inference Using Stochastic Gradient Descent

Many areas of machine learning and data mining focus on point estimates of key parameters. In transportation, however, the inherent variance, and, critically, the need to understand the limits of that variance and the impact it may have, have long been understood to be important. Indeed, variance and other risk measures that capture the cost of the spread around the mean, are critical factors in understanding how people act. Thus they are critical for prediction, as well as for purposes of long term planning, where controlling risk may be equally important to controlling the mean (the point estimate). There has been tremendous progress on large scale optimization techniques to enable the solution of large scale machine learning and data analytics problems. Stochastic Gradient Descent and its variants is probably the most-used large-scale optimization technique for learning. This has not yet seen an impact on the problem of statistical inference - namely, obtaining distributional information that might allow us to control the variance and hence the risk of certain solutions.

• Record URL:
  http://ctr.utexas.edu/research/d-stop/research/projects/
• Supplemental Notes:
  • https://ctr.utexas.edu/wp-content/uploads/144.pdf

Language

• English

Project

• Status: Completed
• Contract Numbers:

  DTRT13-G-UTC58

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Project Managers:

  Bhat, Chandra

• Performing Organizations:

  Data-Supported Transportation Operations and Planning Center

  University of Texas at Austin
  Austin, TX  United States  78701
• Principal Investigators:

  Caramanis, Constantine

• Start Date: 20170301
• Expected Completion Date: 20180831
• Actual Completion Date: 0
• Source Data: 144

Subject/Index Terms

• TRT Terms: Data mining; Long range planning; Optimization; Risk assessment; Statistical inference; Stochastic processes; Variance
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01634959
• Record Type: Research project
• Source Agency: Data-Supported Transportation Operations and Planning Center
• Contract Numbers: DTRT13-G-UTC58
• Files: UTC, RIP
• Created Date: May 18 2017 9:23PM