An Order-Optimal Adaptive Test Plan For Noisy Group Testing Under Unknown Noise Models
Sudeep Salgia, Qing Zhao
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11 Jun 2021
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.
Chairs:
Dionysios Kalogerias
