Near-Optimal Design of Experiments via Regret Minimization
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.
Cite
Text
Allen-Zhu et al. "Near-Optimal Design of Experiments via Regret Minimization." International Conference on Machine Learning, 2017.Markdown
[Allen-Zhu et al. "Near-Optimal Design of Experiments via Regret Minimization." International Conference on Machine Learning, 2017.](https://mlanthology.org/icml/2017/allenzhu2017icml-nearoptimal/)BibTeX
@inproceedings{allenzhu2017icml-nearoptimal,
title = {{Near-Optimal Design of Experiments via Regret Minimization}},
author = {Allen-Zhu, Zeyuan and Li, Yuanzhi and Singh, Aarti and Wang, Yining},
booktitle = {International Conference on Machine Learning},
year = {2017},
pages = {126-135},
volume = {70},
url = {https://mlanthology.org/icml/2017/allenzhu2017icml-nearoptimal/}
}