Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Differentially Private Clustering via Maximum Coverage
Authors: Matthew Jones, Huy L. Nguyen, Thy D Nguyen11555-11563
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present polynomial algorithms with constant multiplicative error and lower additive error than the previous state-of-the-art for each problem. Additionally, our algorithms use a clustering algorithm without differential privacy as a black-box. |
| Researcher Affiliation | Academia | Matthew Jones, Huy L. Nguyen, Thy D Nguyen Northeastern University EMAIL |
| Pseudocode | Yes | Algorithm 1: Maximum Coverage |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes theoretical algorithms and does not mention using any specific publicly available datasets or provide access information for data. |
| Dataset Splits | No | This paper is theoretical and does not describe experiments with data, therefore it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental execution, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental execution, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and describes algorithms, not empirical experiments. Therefore, it does not provide specific experimental setup details like hyperparameters or training configurations. |