ICML 2017poster77 citations
Near-Optimal Design of Experiments via Regret Minimization
Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang
Abstract
We consider computationally tractable methods for the experimental design problem, where k out of n design points of dimension p are selected so that certain optimality criteria are approximately satisfied. Our algorithm finds a $(1+\epsilon)$-approximate optimal design when k is a linear function of p; in contrast, existing results require k to be super-linear in p. Our algorithm also handles all popular optimality criteria, while existing ones only handle one or two such criteria. Numerical results on synthetic and real-world design problems verify the practical effectiveness of the proposed algorithm.
BibTeX
@InProceedings{pmlr-v70-allen-zhu17e,
title = {Near-Optimal Design of Experiments via Regret Minimization},
author = {Zeyuan Allen-Zhu and Yuanzhi Li and Aarti Singh and Yining Wang},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {126--135},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/allen-zhu17e/allen-zhu17e.pdf},
url = {https://proceedings.mlr.press/v70/allen-zhu17e.html},
abstract = {We consider computationally tractable methods for the experimental design problem, where k out of n design points of dimension p are selected so that certain optimality criteria are approximately satisfied. Our algorithm finds a $(1+\epsilon)$-approximate optimal design when k is a linear function of p; in contrast, existing results require k to be super-linear in p. Our algorithm also handles all popular optimality criteria, while existing ones only handle one or two such criteria. Numerical results on synthetic and real-world design problems verify the practical effectiveness of the proposed algorithm.}
}