Adaptive Monte Carlo via bandit allocation
- James Neufeld, Department of Computing Science, University of Alberta
- Andras Gyorgy
- Csaba Szepesvari, Department of Computing Science; PI of AICML
- Dale Schuurmans, AICML
We consider the problem of sequentially choosing between a set of unbiased Monte Carlo estimators to minimize the mean-squared-error (MSE) of a final combined estimate. By reducing this task to a stochastic multi-armed bandit problem, we show that well developed allocation strategies can be used to achieve an MSE that approaches that of the best estimator chosen in retrospect. We then extend these developments to a scenario where alternative estimators have different, possibly stochastic, costs. The outcome is a new set of adaptive Monte Carlo strategies that provide stronger guarantees than previous approaches while offering practical advantages.
Citation
J. Neufeld, A. Gyorgy, C. Szepesvari, D. Schuurmans. "Adaptive Monte Carlo via bandit allocation". International Conference on Machine Learning (ICML), (ed: Eric P. Xing, Tony Jebara), pp 1944-1952, June 2014.| Keywords: | |
| Category: | In Conference |
| Web Links: | PMLR |
BibTeX
@incollection{Neufeld+al:ICML14,
author = {James Neufeld and Andras Gyorgy and Csaba Szepesvari and Dale
Schuurmans},
title = {Adaptive Monte Carlo via bandit allocation},
Editor = {Eric P. Xing, Tony Jebara},
Pages = {1944-1952},
booktitle = {International Conference on Machine Learning (ICML)},
year = 2014,
}Last Updated: February 19, 2020Submitted by Sabina P
