Scalable and Efficient Non-adaptive Deterministic Group Testing
Authors: Dariusz Kowalski, Dominik Pajak
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Collaboration | Dariusz R. Kowalski School of Computer and Cyber Sciences Augusta University, USA dkowalski@augusta.edu Dominik Pajak Department of Pure Mathematics Wroclaw University of Science and Technology, Infermedica, Poland dominik.pajak@pwr.edu.pl |
| Pseudocode | Yes | Algorithm 1: Construction of a sequence of queries solving QGT with -capped feedback. Algorithm 2: Decoding of the elements for QGT with -capped feedback. |
| Open Source Code | No | The paper states in its checklist, under 'Did you include the license to the code and datasets?', that the answer is '[N/A]' and the justification is 'The code and the data are proprietary.'. |
| Open Datasets | No | The paper is theoretical and does not use datasets for empirical evaluation. Therefore, there is no mention of publicly available training data. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, thus no training, validation, or test splits are described. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs, not on implementing and running software for experiments. No specific software dependencies with version numbers are mentioned for empirical work. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |