How to sample if you must: on optimal functional sampling
Machine Learning
2012-08-14 v1 Machine Learning
Abstract
We examine a fundamental problem that models various active sampling setups, such as network tomography. We analyze sampling of a multivariate normal distribution with an unknown expectation that needs to be estimated: in our setup it is possible to sample the distribution from a given set of linear functionals, and the difficulty addressed is how to optimally select the combinations to achieve low estimation error. Although this problem is in the heart of the field of optimal design, no efficient solutions for the case with many functionals exist. We present some bounds and an efficient sub-optimal solution for this problem for more structured sets such as binary functionals that are induced by graph walks.
Cite
@article{arxiv.1208.2417,
title = {How to sample if you must: on optimal functional sampling},
author = {Assaf Hallak and Shie Mannor},
journal= {arXiv preprint arXiv:1208.2417},
year = {2012}
}