An Order-Optimal Adaptive Test Plan for Noisy Group Testing Under Unknown Noise Models

Sudeep Salgia; Qing Zhao

ICASSP 2021accepted0 citations

An Order-Optimal Adaptive Test Plan for Noisy Group Testing Under Unknown Noise Models

Abstract

We consider the problem of noisy group testing where the test results are corrupted by noise with an unknown distribution. We propose an adaptive test plan consisting of a hierarchy of biased random walks guided by a local sequential test which together lend adaptivity and agnosticism to the unknown noise model. We show that the proposed test plan is order optimal in both the population size and the error rate.

BibTeX

@inproceedings{icassp2021_anorderoptimalad,
  title = {An Order-Optimal Adaptive Test Plan for Noisy Group Testing Under Unknown Noise Models},
  author = {Sudeep Salgia and Qing Zhao},
  booktitle = {ICASSP 2021},
  year = {2021}
}