Scalable and Efficient Non-adaptive Deterministic Group Testing

Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track

Bibtex Paper Supplemental


Dariusz Kowalski, Dominik Pajak


Group Testing (GT) is about learning a (hidden) subset $K$, of size $k$, of some large domain $N$, of size $n \gg k$, using a sequence of queries. A result of a query provides some information about the intersection of the query with the unknown set $K$. The goal is to design efficient (polynomial time) and scalable (polylogarithmic number of queries per element in $K$) algorithms for constructing queries that allow to decode every hidden set $K$ based on the results of the queries. A vast majority of the previous work focused on randomized algorithms minimizing the number of queries; however, in case of large domains N, randomization may result in asignificant deviation from the expected precision of learning the set $K$. Others assumed unlimited computational power (existential results) or adaptiveness of queries (next query could be constructed taking into account the results of the previous queries) – the former approach is less practical due to non-efficiency, and the latter has several drawbacks including non-parallelization. To avoid all the abovementioned drawbacks, for Quantitative Group Testing (QGT) where query result is the size of its intersection with the hidden set, we present the first efficient and scalable non-adaptive deterministic algorithms for constructing queries and decoding a hidden set K from the results of the queries – these solutions do not use any randomization, adaptiveness or unlimited computational power.