Noisy Adaptive Group Testing: Bounds and Algorithms

TL;DR

This paper investigates noisy adaptive group testing, establishing bounds and algorithms that operate efficiently with minimal adaptivity stages, especially effective at low noise levels.

Contribution

It provides the first tight bounds and practical algorithms for noisy adaptive group testing with limited adaptivity stages.

Findings

Bounds are tight or near-tight in many regimes.

Algorithms with only two or three adaptive stages are effective.

Results are especially strong at low noise levels.

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 is relevant in applications such as medical testing, communication protocols, pattern matching, and many more. One of the defining features of the group testing problem is the distinction between the non-adaptive and adaptive settings: In the non-adaptive case, all tests must be designed in advance, whereas in the adaptive case, each test can be designed based on the previous outcomes. While tight information-theoretic limits and near-optimal practical algorithms are known for the adaptive setting in the absence of noise, surprisingly little is known in the noisy adaptive setting. In this paper, we address this gap by providing information-theoretic achievability and converse bounds under various noise models, as well as a slightly weaker achievability bound for a computationally efficient variant. These bounds are shown to be tight or near-tight in a broad range of scaling regimes, particularly at low noise levels. The algorithms used for the achievability results have the notable feature of only using two or three stages of adaptivity.