详情

Statistical Inference Using Stochastic Gradient Descent

项目名称：	Statistical Inference Using Stochastic Gradient Descent
摘要：	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.
状态：	Completed
资助组织：	Office of the Assistant Secretary for Research and Technology
项目负责人：	Bhat, Chandra
执行机构：	Data-Supported Transportation Operations and Planning Center
主要研究人员：	Caramanis, Constantine
开始时间：	20170301
预计完成日期：	20180831
实际结束时间：	0