Generalized Framework for Group Testing: Queries, Feedbacks and Adversaries

TL;DR

This paper introduces a unified framework for group testing that characterizes how different feedback properties affect query complexity, providing bounds and analyzing robustness against adversaries and feedback variations.

Contribution

It proposes a generic framework based on input capacity and output expressiveness, deriving bounds on query complexity for various feedback types in group testing.

Findings

Upper bounds on query complexity for efficient feedbacks

Lower bounds for feedbacks with given parameters

Robustness of bounds against adversarial feedback

Abstract

In the Group Testing problem, the objective is to learn a subset K of some much larger domain N, using the shortest-possible sequence of queries Q. A feedback to a query provides some information about the intersection between the query and subset K. Several specific feedbacks have been studied in the literature, often proving different formulas for the estimate of the query complexity of the problem, defined as the shortest length of queries' sequence solving Group Testing problem with specific feedback. In this paper we study what are the properties of the feedback that influence the query complexity of Group Testing and what is their measurable impact. We propose a generic framework that covers a vast majority of relevant settings considered in the literature, which depends on two fundamental parameters of the feedback: input capacity $\alpha$ and output expressiveness $\beta$. They upper bound the logarithm of the size of the feedback function domain and image, respectively. To justify the value of the framework, we prove upper bounds on query complexity of non adaptive, deterministic Group Testing under some "efficient" feedbacks, for minimum, maximum and general expressiveness, and complement them with a lower bound on all feedbacks with given parameters $\alpha,\beta$. Our upper bounds also hold if the feedback function could get an input twisted by a malicious adversary, in case the intersection of a query and the hidden set is bigger than the feedback capacity $\alpha$. We also show that slight change in the feedback function may result in substantial worsening of the query complexity. Additionally, we analyze explicitly constructed randomized counterparts of the deterministic results. Our results provide some insights to what are the most useful bits of information an output-restricted feedback could provide, and open a number of challenging research directions.