Noisy Adaptive Group Testing via Noisy Binary Search

TL;DR

This paper introduces adaptive noisy group testing algorithms based on noisy binary search, achieving optimal test efficiency and practical robustness, with promising numerical results compared to non-adaptive methods.

Contribution

It develops new adaptive algorithms for noisy group testing using noisy binary search, matching the best known test bounds while improving practical performance.

Findings

Algorithms match the best known test bounds.

Adaptive strategies outperform non-adaptive ones in practice.

Numerical experiments show significant reduction in tests needed.

Abstract

The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of possibly-noisy tests, and has numerous practical applications. One of the defining features of group testing is whether the tests are adaptive (i.e., a given test can be chosen based on all previous outcomes) or non-adaptive (i.e., all tests must be chosen in advance). In this paper, building on the success of binary splitting techniques in noiseless group testing (Hwang, 1972), we introduce noisy group testing algorithms that apply noisy binary search as a subroutine. We provide three variations of this approach with increasing complexity, culminating in an algorithm that succeeds using a number of tests that matches the best known previously (Scarlett, 2019), while overcoming fundamental practical limitations of the existing approach, and more precisely capturing the dependence of the number of tests on the error probability. We provide numerical experiments demonstrating that adaptive group testing strategies based on noisy binary search can be highly effective in practice, using significantly fewer tests compared to state-of-the-art non-adaptive strategies.