@inproceedings{021053d0fabf447cbcefee774d0670d6,
title = "Uniform convergence of sample average approximation with adaptive multiple importance sampling",
abstract = "We study sample average approximations under adaptive importance sampling in which the sample densities may depend on previous random samples. Based on a generic uniform law of large numbers, we establish uniform convergence of the sample average approximation to the function being approximated. In the optimization context, we obtain convergence of the optimal value and optimal solutions of the sample average approximation.",
author = "{Ben Feng}, M. and Alvaro Maggiar and Jeremy Staum and Andreas W{\"a}chter",
note = "Funding Information: This research was partially supported by the National Science Foundation of the United States under Grant Number CMMI-1634982. Andreas Waechter was supported in part by the National Science Foundation of the United States under Grant Number DMS-1522747. We thank Tito Homem-de-Mello, David Morton, Imry Rosenbaum, and Johannes Royset for discussions. Publisher Copyright: {\textcopyright} 2018 IEEE; 2018 Winter Simulation Conference, WSC 2018 ; Conference date: 09-12-2018 Through 12-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/WSC.2018.8632370",
language = "English (US)",
series = "Proceedings - Winter Simulation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1646--1657",
booktitle = "WSC 2018 - 2018 Winter Simulation Conference",
address = "United States",
}