@techreport{NBERt0317,
 title = "Generalized Stochastic Gradient Learning",
 author = "George W. Evans and Seppo Honkapohja and Noah Williams",
 institution = "National Bureau of Economic Research",
 type = "Working Paper",
 series = "Technical Working Paper Series",
 number = "317",
 year = "2005",
 month = "October",
 URL = "http://www.nber.org/papers/t0317",
 abstract = {We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG algorithms are sensitive to units of measurement and we show that there is a transformation of variables for which E-stability governs SG stability. GSG algorithms with constant gain have a deeper justification in terms of parameter drift, robustness and risk sensitivity.},
}